diff --git a/MED3pa/detectron/experiment.py b/MED3pa/detectron/experiment.py
index c4aca86..8d420b7 100644
--- a/MED3pa/detectron/experiment.py
+++ b/MED3pa/detectron/experiment.py
@@ -121,6 +121,14 @@ def save(self, file_path: str, file_name: str = 'detectron_results', save_config
         with open(file_name_path, 'w') as file:
             json.dump(self.test_results, file, indent=4)
         
+        counts_dict = {}
+        counts_dict['reference'] = self.cal_record.rejected_counts().tolist()
+        counts_dict['test'] = self.test_record.rejected_counts().tolist()
+        
+        file_name_path_counts = os.path.join(file_path, 'rejection_counts.json')
+        with open(file_name_path_counts, 'w') as file:
+            json.dump(counts_dict, file, indent=4)
+
         if save_config:
             config_file_path = os.path.join(file_path, 'experiment_config.json')
             with open(config_file_path, 'w') as file:
diff --git a/MED3pa/detectron/strategies.py b/MED3pa/detectron/strategies.py
index d6c1385..a2e8b1e 100644
--- a/MED3pa/detectron/strategies.py
+++ b/MED3pa/detectron/strategies.py
@@ -103,26 +103,38 @@ def execute(calibration_records: DetectronRecordsManager, test_records:Detectron
                 
         # Perform the Mann-Whitney U test
         u_statistic, p_value = stats.mannwhitneyu(cal_counts, test_counts, alternative='less')
-        z_score = (test_mean - cal_mean) / cal_std
+        
+        # Calculate the z-scores for the test data
+        z_scores = (test_counts[:, None] - cal_counts) / np.std(cal_counts)
+
+        # Define thresholds for categorizing
+        def categorize_z_score(z):
+            if z <= 0:
+                return 'no significant shift'
+            elif abs(z) < 1:
+                return 'small'
+            elif abs(z) < 2:
+                return 'moderate'
+            else:
+                return 'large'
+
+        # Categorize each test count based on its z-score
+        categories = np.array([categorize_z_score(z) for z in z_scores.flatten()])
+        # Calculate the percentage of each category
+        category_counts = pd.Series(categories).value_counts(normalize=True) * 100
 
         # Describe the significance of the shift based on the z-score
-        significance_description = ""
-        if z_score <= 0 :
-            significance_description = "no significant shift"
-        elif abs(z_score) < 1.0:
-            significance_description = "Small"
-        elif abs(z_score) < 2.0:
-            significance_description = "Moderate"
-        elif abs(z_score) < 3.0:
-            significance_description = "Large"
-        else:
-            significance_description = "Very large"
-        # Results dictionary including rank statistics
+        significance_description = {
+            'no shift': category_counts.get('no significant shift', 0),
+            'small': category_counts.get('small', 0),
+            'moderate': category_counts.get('moderate', 0),
+            'large': category_counts.get('large', 0)
+        }
+
         results = {
             'p_value': p_value,
             'u_statistic': u_statistic,
-            'z-score':z_score,
-            'shift significance' : significance_description
+            'significance_description' : significance_description
         }
 
         return results
@@ -239,7 +251,7 @@ def trim_dataset(data, proportion_to_cut):
         # Define thresholds for categorizing
         def categorize_z_score(z):
             if z <= 0:
-                return 'no shift'
+                return 'no significant shift'
             elif abs(z) < 1:
                 return 'small'
             elif abs(z) < 2:
@@ -257,7 +269,7 @@ def categorize_z_score(z):
 
         # Describe the significance of the shift based on the z-score
         significance_description = {
-            'no shift': category_counts.get('no shift', 0),
+            'no shift': category_counts.get('no significant shift', 0),
             'small': category_counts.get('small', 0),
             'moderate': category_counts.get('moderate', 0),
             'large': category_counts.get('large', 0)
diff --git a/MED3pa/med3pa/comparaison.py b/MED3pa/med3pa/comparaison.py
new file mode 100644
index 0000000..bbff10f
--- /dev/null
+++ b/MED3pa/med3pa/comparaison.py
@@ -0,0 +1,198 @@
+import json
+import os
+from typing import Any, Dict, List, Tuple, Type, Union
+
+import numpy as np
+from sklearn.model_selection import train_test_split
+
+from MED3pa.med3pa.models import *
+from MED3pa.med3pa.uncertainty import *
+from MED3pa.models.base import BaseModelManager
+from MED3pa.med3pa.experiment import Med3paResults
+
+class Med3paComparison:
+    """
+    Class to compare the results of two Med3paExperiment instances.
+    """
+    def __init__(self, results1_path: str, results2_path: str) -> None:
+        self.results1_path = os.path.abspath(results1_path)
+        self.results2_path = os.path.abspath(results2_path)
+        self.profiles_metrics_comparaison = {}
+        self.profiles_detectron_comparaison = {}
+        self.global_metrics_comparaison = {}
+        self.compare_profiles = False
+        self.compare_detectron = False
+        self._check_experiment_name()
+
+    def _check_experiment_name(self) -> None:
+        """
+        Checks if the experiment_name in the config_file of both results paths is the same.
+        If not, raises a ValueError. Also sets the flag for Detectron comparison if applicable.
+        """
+        config_file_1 = os.path.join(self.results1_path, 'experiment_config.json')
+        config_file_2 = os.path.join(self.results2_path, 'experiment_config.json')
+
+        with open(config_file_1, 'r') as f1, open(config_file_2, 'r') as f2:
+            config1 = json.load(f1)
+            config2 = json.load(f2)
+
+        if config1['experiment_name'] not in ["Med3paDetectronExperiment", "Med3paExperiment"]:
+            raise ValueError("Only Med3paDetectronExperiments & Med3paExperiments can be compared")
+        
+        if config1['experiment_name'] != config2['experiment_name']:
+            raise ValueError("The two results are not from the same experiment.")
+        
+        if config1['experiment_name'] == 'Med3paDetectronExperiment':
+            self.compare_detectron = True
+    
+    def _check_experiment_tree(self) -> None:
+        """
+        Checks if the experiment trees in the results paths are the same.
+        If they are, sets the flag for profile comparison.
+        """
+        tree_file_1 = os.path.join(self.results1_path, 'test', 'tree.json')
+        tree_file_2 = os.path.join(self.results2_path, 'test', 'tree.json')
+
+        with open(tree_file_1, 'r') as f1, open(tree_file_2, 'r') as f2:
+            tree1 = json.load(f1)
+            tree2 = json.load(f2)
+
+        if tree1 == tree2:
+            self.compare_profiles = True
+
+    def compare_profiles_metrics(self):
+        """
+        Compares profile metrics between two sets of results and stores them in a dictionary.
+        """
+        combined = {}
+        profiles_file_1 = os.path.join(self.results1_path, 'test', 'profiles.json')
+        profiles_file_2 = os.path.join(self.results2_path, 'test', 'profiles.json')
+
+        with open(profiles_file_1, 'r') as f1, open(profiles_file_2, 'r') as f2:
+            profiles1 = json.load(f1)
+            profiles2 = json.load(f2)
+
+        for samples_ratio, dr_dict in profiles1.items():
+            if samples_ratio not in combined:
+                combined[samples_ratio] = {}
+            for dr, profiles in dr_dict.items():
+                for profile in profiles:
+                    profile_path = " / ".join(profile["path"])
+                    if profile_path not in combined[samples_ratio]:
+                        combined[samples_ratio][profile_path] = {}
+                    if dr not in combined[samples_ratio][profile_path]:
+                        combined[samples_ratio][profile_path][dr] = {}
+                    combined[samples_ratio][profile_path][dr]['metrics_1'] = profile["metrics"]
+
+        for samples_ratio, dr_dict in profiles2.items():
+            if samples_ratio not in combined:
+                combined[samples_ratio] = {}
+            for dr, profiles in dr_dict.items():
+                for profile in profiles:
+                    profile_path = " / ".join(profile["path"])
+                    if profile_path not in combined[samples_ratio]:
+                        combined[samples_ratio][profile_path] = {}
+                    if dr not in combined[samples_ratio][profile_path]:
+                        combined[samples_ratio][profile_path][dr] = {}
+                    combined[samples_ratio][profile_path][dr]['metrics_2'] = profile["metrics"]
+
+        self.profiles_metrics_comparaison = combined
+    
+    def compare_profiles_detectron_results(self):
+        """
+        Compares Detectron results between two sets of profiles and stores them in a dictionary.
+        """
+        combined = {}
+        profiles_file_1 = os.path.join(self.results1_path, 'test', 'profiles.json')
+        profiles_file_2 = os.path.join(self.results2_path, 'test', 'profiles.json')
+
+        with open(profiles_file_1, 'r') as f1, open(profiles_file_2, 'r') as f2:
+            profiles1 = json.load(f1)
+            profiles2 = json.load(f2)
+
+        # Determine the smallest positive samples_ratio
+        smallest_samples_ratio = min(filter(lambda x: float(x) > 0, profiles1.keys()))
+
+        for profiles in [profiles1, profiles2]:
+            if smallest_samples_ratio not in profiles:
+                continue
+            
+            dr_dict = profiles[smallest_samples_ratio]
+            
+            if smallest_samples_ratio not in combined:
+                combined[smallest_samples_ratio] = {}
+            
+            if "100" not in dr_dict:
+                continue
+            
+            for profile in dr_dict["100"]:
+                profile_path = " / ".join(profile["path"])
+                if profile_path not in combined[smallest_samples_ratio]:
+                    combined[smallest_samples_ratio][profile_path] = {}
+                if "100" not in combined[smallest_samples_ratio][profile_path]:
+                    combined[smallest_samples_ratio][profile_path]["100"] = {}
+                
+                if profiles is profiles1:
+                    combined[smallest_samples_ratio][profile_path]["100"]['detectron_results_1'] = profile["detectron_results"]
+                else:
+                    combined[smallest_samples_ratio][profile_path]["100"]['detectron_results_2'] = profile["detectron_results"]
+
+        self.profiles_detectron_comparaison = combined
+
+    def compare_global_metrics(self):
+        """
+        Compares global metrics between two sets of results and stores them in a dictionary.
+        """
+        combined = {}
+        file_1 = os.path.join(self.results1_path, 'test', 'metrics_dr.json')
+        file_2 = os.path.join(self.results2_path, 'test', 'metrics_dr.json')
+
+        with open(file_1, 'r') as f1, open(file_2, 'r') as f2:
+            dr1 = json.load(f1)
+            dr2 = json.load(f2)
+        
+        for dr in range(100, -1, -1):  # Iterating from 100 to 0
+            dr_str = str(dr)
+            combined[dr_str] = {}
+            
+            if dr_str in dr1:
+                combined[dr_str]['metrics_dr_1'] = dr1[dr_str]
+            if dr_str in dr2:
+                combined[dr_str]['metrics_dr_2'] = dr2[dr_str]
+
+        self.global_metrics_comparaison = combined
+
+    def compare_experiments(self):
+        """
+        Compares the experiments by global metrics, profiles, and Detectron results if applicable.
+        """
+        self.compare_global_metrics()
+        self._check_experiment_tree()
+        if self.compare_profiles:
+            self.compare_profiles_metrics()
+        if self.compare_detectron:
+            self.compare_profiles_detectron_results()
+
+    def save(self, directory_path: str) -> None:
+        """
+        Saves the comparison results to a specified directory.
+
+        Args:
+            directory_path (str): The directory where the comparison results will be saved.
+        """
+        # Ensure the main directory exists
+        os.makedirs(directory_path, exist_ok=True)
+
+        global_comparaison_path = os.path.join(directory_path, 'global_metrics_comparaison.json')
+        with open(global_comparaison_path, 'w') as f:
+                json.dump(self.global_metrics_comparaison, f, indent=4)
+
+        if self.profiles_detectron_comparaison is not {} and self.compare_detectron:
+            profiles_detectron_path = os.path.join(directory_path, 'profiles_detectron_comparaison.json')
+            with open(profiles_detectron_path, 'w') as f:
+                json.dump(self.profiles_detectron_comparaison, f, indent=4)
+        
+        if self.profiles_metrics_comparaison is not {} and self.compare_profiles:
+            profiles_metrics_path = os.path.join(directory_path, 'profiles_metrics_comparaison.json')
+            with open(profiles_metrics_path, 'w') as f:
+                json.dump(self.profiles_metrics_comparaison, f, indent=4)
\ No newline at end of file
diff --git a/MED3pa/med3pa/experiment.py b/MED3pa/med3pa/experiment.py
index d9d7098..2191e1a 100644
--- a/MED3pa/med3pa/experiment.py
+++ b/MED3pa/med3pa/experiment.py
@@ -54,6 +54,8 @@ def __init__(self) -> None:
         self.profiles_manager: ProfilesManager = None
         self.datasets: Dict[int, MaskedDataset] = {}
         self.experiment_config = {}
+        self.min_confidence_levels_thresholds={}
+        self.tree = {}
     
     def set_metrics_by_dr(self, metrics_by_dr: Dict) -> None:
         """
@@ -73,6 +75,9 @@ def set_profiles_manager(self, profile_manager : ProfilesManager) -> None:
         """
         self.profiles_manager = profile_manager
 
+    def set_confidence_levels_thresholds(self, thresholds: dict, samples_ratio: int):
+        self.min_confidence_levels_thresholds[samples_ratio] = thresholds
+    
     def set_models_evaluation(self, ipc_evaluation: Dict, apc_evaluation: Dict=None) -> None:
         """
         Set models evaluation metrics.
@@ -86,7 +91,13 @@ def set_models_evaluation(self, ipc_evaluation: Dict, apc_evaluation: Dict=None)
         if apc_evaluation is not None:
             self.models_evaluation['APC_evaluation'] = apc_evaluation
     
-    def set_dataset(self, samples_ratio: int, dataset: MaskedDataset) -> None:
+    def set_tree(self, tree:TreeRepresentation):
+        """
+        Sets the constructed tree
+        """
+        self.tree = tree
+
+    def set_dataset(self, mode: str, dataset: MaskedDataset) -> None:
         """
         Saves the dataset for a given sample ratio.
 
@@ -95,7 +106,7 @@ def set_dataset(self, samples_ratio: int, dataset: MaskedDataset) -> None:
             dataset (MaskedDataset): The MaskedDataset instance.
         """
 
-        self.datasets[samples_ratio] = dataset
+        self.datasets[mode] = dataset
 
     def save(self, file_path: str) -> None:
         """
@@ -125,10 +136,19 @@ def save(self, file_path: str) -> None:
             with open(models_evaluation_file_path, 'w') as file:
                 json.dump(self.models_evaluation, file, default=to_serializable, indent=4)
         
-        for samples_ratio, dataset in self.datasets.items():
-            dataset_path = os.path.join(file_path, f'dataset_{samples_ratio}.csv')
+        for mode, dataset in self.datasets.items():
+            dataset_path = os.path.join(file_path, f'dataset_{mode}.csv')
             dataset.save_to_csv(dataset_path)
 
+        if self.min_confidence_levels_thresholds is not {}:
+            min_confidence_levels_path = os.path.join(file_path, 'min_confidence_levels.json')
+            with open(min_confidence_levels_path, 'w') as file:
+                json.dump(self.min_confidence_levels_thresholds, file, default=to_serializable, indent=4)
+
+        if self.tree is not None: 
+            tree_path = os.path.join(file_path, 'tree.json')
+            self.tree.save_tree(tree_path)
+    
     def get_profiles_manager(self) -> ProfilesManager:
         """
         Retrieves the profiles manager for this Med3paResults instance
@@ -145,7 +165,7 @@ def __init__(self, reference_record:Med3paRecord, test_record:Med3paRecord) -> N
         self.test_record = test_record
         self.experiment_config = {}
         self.detectron_results = None
-    
+
     def set_detectron_results(self, detectron_results: DetectronResult=None) -> None:
         """
         Sets the detectron results for the Med3paDetectron experiment.
@@ -203,6 +223,7 @@ def run(datasets_manager: DatasetsManager,
             apc_params: Dict = None,
             apc_grid_params: Dict = None,
             apc_cv: int = 4,
+            fixed_tree:str = None,
             samples_ratio_min: int = 0,
             samples_ratio_max: int = 50, 
             samples_ratio_step: int = 5,
@@ -224,6 +245,7 @@ def run(datasets_manager: DatasetsManager,
             apc_params (dict, optional): Parameters for initializing the APC regressor model, by default None.
             apc_grid_params (dict, optional): Grid search parameters for optimizing the APC model, by default None.
             apc_cv (int, optional): Number of cross-validation folds for optimizing the APC model, by default None.
+            fixed_tree (int, optional): a tree structure to use, by default None.
             samples_ratio_min (int, optional): Minimum sample ratio, by default 0.
             samples_ratio_max (int, optional): Maximum sample ratio, by default 50.
             samples_ratio_step (int, optional): Step size for sample ratio, by default 5.
@@ -236,16 +258,16 @@ def run(datasets_manager: DatasetsManager,
         """
         print("Running MED3pa Experiment on the reference set:")
         results_reference, ipc_config, apc_config = Med3paExperiment._run_by_set(datasets_manager=datasets_manager,set= 'reference',base_model_manager= base_model_manager, 
-                                                         uncertainty_metric=uncertainty_metric,
-                                                         ipc_type=ipc_type, ipc_params=ipc_params, ipc_grid_params=ipc_grid_params, ipc_cv=ipc_cv, 
+                                                         uncertainty_metric=uncertainty_metric, fixed_tree=fixed_tree,
+                                                         ipc_type=ipc_type, ipc_params=ipc_params, ipc_grid_params=ipc_grid_params, ipc_cv=ipc_cv,
                                                          apc_params=apc_params,apc_grid_params=apc_grid_params, apc_cv=apc_cv, 
                                                          samples_ratio_min=samples_ratio_min, samples_ratio_max=samples_ratio_max, samples_ratio_step=samples_ratio_step, 
                                                          med3pa_metrics=med3pa_metrics, evaluate_models=evaluate_models, models_metrics=models_metrics, mode=mode)
-        print("Running MED3pa Experiment on the reference set:")
-        results_testing, ipc_config, apc_config = Med3paExperiment._run_by_set(datasets_manager=datasets_manager,set= 'testing',base_model_manager= base_model_manager, 
-                                                         uncertainty_metric=uncertainty_metric,
+        print("Running MED3pa Experiment on the test set:")
+        results_testing, _, _ = Med3paExperiment._run_by_set(datasets_manager=datasets_manager,set= 'testing',base_model_manager= base_model_manager, 
+                                                         uncertainty_metric=uncertainty_metric, fixed_tree=fixed_tree,
                                                          ipc_type=ipc_type, ipc_params=ipc_params, ipc_grid_params=ipc_grid_params, ipc_cv=ipc_cv, 
-                                                         apc_params=apc_params,apc_grid_params=apc_grid_params, apc_cv=apc_cv, 
+                                                         apc_params=apc_params,apc_grid_params=apc_grid_params, apc_cv=apc_cv,
                                                          samples_ratio_min=samples_ratio_min, samples_ratio_max=samples_ratio_max, samples_ratio_step=samples_ratio_step, 
                                                          med3pa_metrics=med3pa_metrics, evaluate_models=evaluate_models, models_metrics=models_metrics, mode=mode)
         
@@ -255,8 +277,8 @@ def run(datasets_manager: DatasetsManager,
             'datasets':datasets_manager.get_info(),
             'base_model': base_model_manager.get_instance().get_info(),
             'uncertainty_metric': uncertainty_metric,
-            'ipc_model': ipc_config,
-            'apc_model': apc_config,
+            'ipc_model': ipc_config.get_info(),
+            'apc_model': apc_config.get_info(),
             'samples_ratio_min': samples_ratio_min,
             'samples_ratio_max': samples_ratio_max,
             'samples_ratio_step': samples_ratio_step,
@@ -281,6 +303,7 @@ def _run_by_set(datasets_manager: DatasetsManager,
             apc_params: Dict = None,
             apc_grid_params: Dict = None,
             apc_cv: int = 4,
+            fixed_tree:str = None,
             samples_ratio_min: int = 0,
             samples_ratio_max: int = 50, 
             samples_ratio_step: int = 5,
@@ -289,7 +312,8 @@ def _run_by_set(datasets_manager: DatasetsManager,
             mode: str = 'mpc',
             models_metrics: List[str] = ['MSE', 'RMSE']) -> Tuple[Med3paRecord, dict, dict]:
         
-        """Orchestrates the MED3PA experiment on one specific set of the dataset.
+        """
+        Orchestrates the MED3PA experiment on one specific set of the dataset.
 
         Args:
             datasets_manager (DatasetsManager): the datasets manager containing the dataset to use in the experiment.
@@ -312,26 +336,18 @@ def _run_by_set(datasets_manager: DatasetsManager,
         Returns:
             Med3paRecord: the results of the MED3PA experiment.
         """
-        # retrieve the dataset based on the set type
-        try:
-            if set == 'reference':
-                dataset = datasets_manager.get_dataset_by_type(dataset_type="reference", return_instance=True)
-            elif set == 'testing':
-                dataset = datasets_manager.get_dataset_by_type(dataset_type="testing", return_instance=True)
-            else:
-                raise ValueError("The set must be either the reference set or the testing set")
-        except ValueError as e:  
-            dataset = None
-
-        if dataset is None:
-            return None
         
-        valid_modes = ['mpc', 'apc', 'ipc']
+        # Step 1 : datasets and base model setting
 
-        if mode not in valid_modes:
-            raise ValueError(f"Invalid mode '{mode}'. The mode must be one of {valid_modes}.")
+        # Retrieve the dataset based on the set type
+        if set == 'reference':
+            dataset = datasets_manager.get_dataset_by_type(dataset_type="reference", return_instance=True)
+        elif set == 'testing':
+            dataset = datasets_manager.get_dataset_by_type(dataset_type="testing", return_instance=True)
+        else:
+            raise ValueError("The set must be either the reference set or the testing set")
         
-        # retrieve different dataset components to calculate the metrics
+        # retrieve different dataset components needed for the experiment
         x = dataset.get_observations()
         y_true = dataset.get_true_labels()
         predicted_probabilities = dataset.get_pseudo_probabilities()
@@ -345,97 +361,114 @@ def _run_by_set(datasets_manager: DatasetsManager,
             base_model = base_model_manager.get_instance()
             predicted_probabilities = base_model.predict(x, True)
 
-        dataset.set_pseudo_labels(predicted_probabilities)
+        dataset.set_pseudo_probs_labels(predicted_probabilities, 0.5)
+
+        # Step 2 : Mode and metrics setup
+        valid_modes = ['mpc', 'apc', 'ipc']
+        if mode not in valid_modes:
+            raise ValueError(f"Invalid mode '{mode}'. The mode must be one of {valid_modes}.")
+
+        if med3pa_metrics == []:
+            med3pa_metrics = ClassificationEvaluationMetrics.supported_metrics()
 
-        # Calculate uncertainty values
+        # Step 3 : Calculate uncertainty values
         uncertainty_calc = UncertaintyCalculator(uncertainty_metric)
         uncertainty_values = uncertainty_calc.calculate_uncertainty(x, predicted_probabilities, y_true)
-
-        # set predicted labels
-        dataset.set_pseudo_probs_labels(predicted_probabilities, 0.5)
         
+        # Step 4: Set up splits to evaluate the models
         if evaluate_models:
-            x_train, x_test, uncertainty_train, uncertainty_test = train_test_split(x, uncertainty_values, test_size=0.1, random_state=42)
-        else:
-            x_train = x
-            uncertainty_train = uncertainty_values
+            _, x_test, _, uncertainty_test = train_test_split(x, uncertainty_values, test_size=0.1, random_state=42)
 
-        if med3pa_metrics == []:
-            med3pa_metrics = ClassificationEvaluationMetrics.supported_metrics()
+        x_train = x
+        uncertainty_train = uncertainty_values
+
+       
         
         results = Med3paRecord()
 
-        # Create and train IPCModel
+        # Step 5: Create and train IPCModel
         IPC_model = IPCModel(ipc_type, ipc_params)
         IPC_model.train(x_train, uncertainty_train)
-        print("IPC Model training completed.")
+        print("IPC Model training complete.")
         
         # optimize IPC model if grid params were provided
         if ipc_grid_params is not None:
             IPC_model.optimize(ipc_grid_params, ipc_cv, x_train, uncertainty_train)
-            print("IPC Model optimization done.")
+            print("IPC Model optimization complete.")
 
         # Predict IPC values
         IPC_values = IPC_model.predict(x)
-        
+        print("Individualized confidence scores calculated.")
+
         if mode in ['mpc', 'apc']:
-            # Create and train APCModel
-            APC_model = APCModel(features, apc_params)
+
+            # Step 6: Create and train APCModel
+            APC_model = APCModel(features, apc_params, fixed_tree)
             APC_model.train(x, IPC_values)
-            print("APC Model training completed.")
+            print("APC Model training complete.")
 
             # optimize APC model if grid params were provided
             if apc_grid_params is not None:
                 APC_model.optimize(apc_grid_params, apc_cv, x_train, uncertainty_train)
-                print("APC Model optimization done.")
-
-            profiles_manager = ProfilesManager(features)
-
-            for samples_ratio in range(samples_ratio_min, samples_ratio_max + 1, samples_ratio_step):
-                
-                # Predict APC values
-                APC_values = APC_model.predict(x, min_samples_ratio=samples_ratio)
-
-                if mode == 'mpc':
-                    # Create and predict MPC values
-                    MPC_model = MPCModel(IPC_values=IPC_values, APC_values=APC_values)
-                else:
-                    MPC_model = MPCModel(APC_values=APC_values)
-
+                print("APC Model optimization complete.")
+            
+            # Predict APC values
+            APC_values = APC_model.predict(x)
+            print("Aggregated confidence scores calculated.")
+            # Save the tree structure created by the APCModel
+            tree = APC_model.treeRepresentation
+            results.set_tree(tree=tree)
+            # Save the calculated confidence scores by the APCmodel
+            dataset.set_confidence_scores(APC_values)
+            cloned_dataset = dataset.clone()
+            results.set_dataset(mode="apc", dataset=cloned_dataset)
+            
+            # Step 7: Create and train MPCModel
+            if mode == 'mpc':
+                # Create and predict MPC values
+                MPC_model = MPCModel(IPC_values=IPC_values, APC_values=APC_values)
                 MPC_values = MPC_model.predict()
+                # Save the calculated confidence scores by the MPCmodel
                 dataset.set_confidence_scores(MPC_values)
-                
-                print("Confidence scores calculated for minimum_samples_ratio = ", samples_ratio)
-
-                # Calculate profiles and their metrics by declaration rate
-                tree = APC_model.treeRepresentation
-                MDRCalculator.calc_profiles(profiles_manager, tree, MPC_values, samples_ratio)
-                MDRCalculator.calc_metrics_by_profiles(profiles_manager, datasets_manager, med3pa_metrics, set=set)
-
                 cloned_dataset = dataset.clone()
-                results.set_profiles_manager(profiles_manager)
-                results.set_dataset(samples_ratio=samples_ratio, dataset=cloned_dataset)
-                
+                results.set_dataset(mode="mpc", dataset=cloned_dataset)
+            else:
+                MPC_model = MPCModel(APC_values=APC_values)
+                MPC_values = MPC_model.predict()
+        
+            print("Mixed confidence scores calculated.")
+            
+            # Step 8: Calculate the profiles for the different samples_ratio and drs
+            profiles_manager = ProfilesManager(features)
+            for samples_ratio in range(samples_ratio_min, samples_ratio_max + 1, samples_ratio_step):
+
+                # Calculate profiles and their metrics by declaration rate                
+                MDRCalculator.calc_profiles(profiles_manager, tree, datasets_manager, MPC_values, samples_ratio, set=set)                
+                MDRCalculator.calc_metrics_by_profiles(profiles_manager, datasets_manager, MPC_values, samples_ratio, med3pa_metrics, set=set)
+                results.set_profiles_manager(profiles_manager)                
                 print("Results extracted for minimum_samples_ratio = ", samples_ratio)
         
+        
         # Calculate metrics by declaration rate
         # Create and predict MPC values using only the IPC values
         MPC_model = MPCModel(IPC_values=IPC_values)
         MPC_values = MPC_model.predict()
-        
+        # Save the confidence scores predicted by the IPCModel
         dataset.set_confidence_scores(MPC_values)
-        metrics_by_dr = MDRCalculator.calc_metrics_by_dr(datasets_manager=datasets_manager, metrics_list=med3pa_metrics, set=set)
+        cloned_dataset = dataset.clone()
+        results.set_dataset(mode="ipc", dataset=cloned_dataset)
+        metrics_by_dr = MDRCalculator.calc_metrics_by_dr(datasets_manager=datasets_manager, confidence_scores=MPC_values, metrics_list=med3pa_metrics, set=set)
         results.set_metrics_by_dr(metrics_by_dr)
 
         if mode in ['mpc', 'apc']:
-            ipc_config = IPC_model.get_info()
-            apc_config = APC_model.get_info() 
+            ipc_config = IPC_model
+            apc_config = APC_model 
             if evaluate_models:
                 IPC_evaluation = IPC_model.evaluate(x_test, uncertainty_test, models_metrics)
                 APC_evaluation = APC_model.evaluate(x_test, uncertainty_test, models_metrics)
                 results.set_models_evaluation(IPC_evaluation, APC_evaluation)
         else:
-            ipc_config = IPC_model.get_info()
+            ipc_config = IPC_model
             apc_config = None
             if evaluate_models:
                 IPC_evaluation = IPC_model.evaluate(x_test, uncertainty_test, models_metrics)
@@ -468,7 +501,7 @@ def run(datasets: DatasetsManager,
             samples_ratio_min: int = 0,
             samples_ratio_max: int = 50, 
             samples_ratio_step: int = 5,
-            med3pa_metrics: List[str] = ['Auc', 'Accuracy', 'BalancedAccuracy'],
+            med3pa_metrics: List[str] = [],
             evaluate_models: bool = False,
             models_metrics: List[str] = ['MSE', 'RMSE'],
             mode: str = 'mpc',
diff --git a/MED3pa/med3pa/mdr.py b/MED3pa/med3pa/mdr.py
index e640b17..2fdd580 100644
--- a/MED3pa/med3pa/mdr.py
+++ b/MED3pa/med3pa/mdr.py
@@ -12,6 +12,7 @@
 from MED3pa.models import BaseModelManager
 from MED3pa.models.classification_metrics import *
 
+from pprint import pprint
 
 class MDRCalculator:
     """
@@ -82,10 +83,10 @@ def _list_difference_by_key(list1: List[Profile], list2: List[Profile], key='nod
         Returns:
             List[Profile]: A list containing elements from list1 that do not appear in list2 based on the specified key.
         """
-        set1 = {getattr(d, key) for d in list1 if hasattr(d, key)}
-        set2 = {getattr(d, key) for d in list2 if hasattr(d, key)}
+        set1 = {d[key] for d in list1 if key in d}
+        set2 = {d[key] for d in list2 if key in d}
         unique_to_list1 = set1 - set2
-        return [d for d in list1 if getattr(d, key) in unique_to_list1]
+        return [d for d in list1 if d[key] in unique_to_list1]
 
     @staticmethod
     def _filter_by_profile(datasets_manager : DatasetsManager, path : List, set:str = 'reference', min_confidence_level:float = None):
@@ -168,7 +169,7 @@ def _filter_by_profile(datasets_manager : DatasetsManager, path : List, set:str
         return filtered_x, filtered_y_true, filtered_prob, filtered_y_pred, filtered_confidence_scores
             
     @staticmethod
-    def calc_metrics_by_dr(datasets_manager : DatasetsManager, metrics_list: list, set = 'reference'):
+    def calc_metrics_by_dr(datasets_manager: DatasetsManager, confidence_scores: np.ndarray, metrics_list: list, set = 'reference'):
         """
         Calculate metrics by declaration rates (DR), evaluating model performance at various thresholds of predicted accuracies.
 
@@ -192,7 +193,6 @@ def calc_metrics_by_dr(datasets_manager : DatasetsManager, metrics_list: list, s
         y_true = dataset.get_true_labels()
         y_pred = dataset.get_pseudo_labels()
         predicted_prob = dataset.get_pseudo_probabilities()
-        confidence_scores = dataset.get_confidence_scores()
 
         # initialize the dictionaries used for results storage
         metrics_by_dr = {} # global dictionary containing all the declaration rates and their corresponding metrics
@@ -218,7 +218,9 @@ def calc_metrics_by_dr(datasets_manager : DatasetsManager, metrics_list: list, s
                 
                 # save the left population percentage
                 dr_values['population_percentage'] = sum(confidence_mask) / len(confidence_scores)
-                
+                dr_values['mean_confidence_level'] = np.mean(confidence_scores[confidence_mask]) if confidence_scores[confidence_mask].size>0 else None
+                dr_values['Positive%'] = np.sum(y_true[confidence_mask]) / len(y_true[confidence_mask]) * 100 if \
+                        len(y_true[confidence_mask]) > 0 else None
                 # Calculate the metrics for the current DR
                 metrics_dict = MDRCalculator._calculate_metrics(y_true[confidence_mask], y_pred[confidence_mask], predicted_prob[confidence_mask], metrics_list)
                 
@@ -239,7 +241,7 @@ def calc_metrics_by_dr(datasets_manager : DatasetsManager, metrics_list: list, s
         return metrics_by_dr
     
     @staticmethod
-    def calc_profiles(profiles_manager: ProfilesManager, tree: TreeRepresentation, confidence_scores: np.ndarray, min_samples_ratio: int):
+    def calc_profiles_deprecated(profiles_manager: ProfilesManager, tree: TreeRepresentation, confidence_scores: np.ndarray, min_samples_ratio: int):
         """
         Calculates profiles for different declaration rates and minimum sample ratios. This method assesses how profiles change
         across different confidence levels derived from predicted accuracies.
@@ -256,11 +258,13 @@ def calc_profiles(profiles_manager: ProfilesManager, tree: TreeRepresentation, c
         lost_profiles_all = [] # saves last lost profiles
         last_min_confidence_level = -1 # last min_confidence
         last_dr = 100 # last dr
+        min_confidence_levels_dict = {} # saves the min_confidence_level thresholds
 
         # go throught all declaration rates
         for dr in range(100, -1, -1):
             # calculate the min confidence level for this dr
             min_confidence_level = MDRCalculator._get_min_confidence_score(dr, confidence_scores)
+            min_confidence_levels_dict[dr] = min_confidence_level
             # if the current confidence level is different from the last one
             if last_min_confidence_level != min_confidence_level:
                 # update the last min_confidence level
@@ -288,9 +292,81 @@ def calc_profiles(profiles_manager: ProfilesManager, tree: TreeRepresentation, c
             # use the last dr results
             profiles_manager.insert_profiles(dr, min_samples_ratio, profiles_current)
             profiles_manager.insert_lost_profiles(dr, min_samples_ratio, lost_profiles_all)
+        
+        return min_confidence_levels_dict
+
+    def calc_profiles(profiles_manager: ProfilesManager, tree: TreeRepresentation, datasets_manager, confidence_scores: np.ndarray, min_samples_ratio: int, set:str ='reference') -> Dict[int, float]:
+        """
+        Calculates profiles for different declaration rates and minimum sample ratios. This method assesses how profiles change
+        across different confidence levels derived from predicted accuracies.
+
+        Args:
+            profiles_manager (ProfilesManager): Manager for storing and retrieving profile information.
+            tree (TreeRepresentation): Tree structure from which profiles are derived.
+            datasets_manager (DatasetsManager): Manager for handling datasets.
+            confidence_scores (np.ndarray): Array of predicted accuracy values used for thresholding profiles.
+            min_samples_ratio (int): Minimum sample ratio to consider for including a profile.
 
+        Returns:
+            Dict[int, float]: A dictionary with declaration rates as keys and their corresponding minimum confidence levels as values.
+        """
+        
+        # Initialization of different variables
+        all_nodes = tree.get_all_nodes()  # Retrieve all nodes from the tree
+        last_profiles = all_nodes  # Initialize last profiles as all nodes
+        lost_profiles_all = []  # Saves lost profiles
+        last_min_confidence_level = -1  # Last min confidence level
+        min_confidence_levels_dict = {}  # Saves the min_confidence_level thresholds
+
+        # Go through all declaration rates
+        for dr in range(100, -1, -1):
+            
+            # Calculate the min confidence level for this dr
+            min_confidence_level = MDRCalculator._get_min_confidence_score(dr, confidence_scores)
+            min_confidence_levels_dict[dr] = min_confidence_level
+
+            # If the current confidence level is different from the last one
+            if min_confidence_level!=last_min_confidence_level:
+                
+                # Update the last min confidence level
+                last_min_confidence_level = min_confidence_level
+                # Saves the profiles of this dr
+                profiles_current = []
+
+                # Calculate mean_ca and samples_ratio for all nodes to see if this node is eligible as a profile
+                for node in all_nodes:
+                    # filter the data that belongs to this node, and filter according to min_confidence_level threshold
+                    _, _, _, _, filtered_confidence_scores = MDRCalculator._filter_by_profile(
+                    datasets_manager, node['path'], set=set, min_confidence_level=min_confidence_level)
+                    
+                    # calculate the samples_ratio (pop%) and mean_confidence_level of this node, if the filtered data isnt empty
+                    if len(filtered_confidence_scores) > 0:
+                        samples_ratio = len(filtered_confidence_scores) / len(confidence_scores) * 100
+                        mean_cconfidence = np.mean(filtered_confidence_scores) if filtered_confidence_scores.size > 0 else 0
+                        # if the calculated samples_ratio and mean_confidence meet the conditions, keep this node
+                        if samples_ratio >= min_samples_ratio and mean_cconfidence >= min_confidence_level:
+                            profiles_current.append(node)
+                    
+
+                # If the last profiles are different from current profiles
+                if len(last_profiles) != len(profiles_current):
+                    # Extract lost profiles
+                    lost_profiles = MDRCalculator._list_difference_by_key(last_profiles, profiles_current)
+                    lost_profiles_all.extend(lost_profiles)
+                    
+            # Update the last profiles
+            last_profiles = profiles_current
+
+            # If the current min_confidence is same as the last one, use the last dr results
+            profiles_current_ins = profiles_manager.transform_to_profiles(profiles_current)
+            lost_profiles_current_ins = profiles_manager.transform_to_profiles(lost_profiles_all)
+            profiles_manager.insert_profiles(dr, min_samples_ratio, profiles_current_ins)
+            profiles_manager.insert_lost_profiles(dr, min_samples_ratio, lost_profiles_current_ins)
+
+        return min_confidence_levels_dict
+    
     @staticmethod
-    def calc_metrics_by_profiles(profiles_manager, datasets_manager : DatasetsManager, metrics_list, set = 'reference'):
+    def calc_metrics_by_profiles(profiles_manager, datasets_manager : DatasetsManager, confidence_scores: np.ndarray, min_samples_ratio: int, metrics_list, set = 'reference'):
         """
         Calculates various metrics for different profiles and declaration rates based on provided datasets.
 
@@ -311,13 +387,14 @@ def calc_metrics_by_profiles(profiles_manager, datasets_manager : DatasetsManage
 
         # retrieve different dataset components to calculate the metrics
         all_y_true = dataset.get_true_labels()
-        all_confidence_scores = dataset.get_confidence_scores()
+        all_confidence_scores = confidence_scores
+
+        dr_dict = profiles_manager.profiles_records.get(min_samples_ratio)
 
         # go through all profiles, for each ratio and for each dr
-        for ratio, dr_dict in profiles_manager.profiles_records.items():
+        if dr_dict is not None:
             # for each dr and its profiles stored in the ratio
             for dr, profiles in dr_dict.items():
-                
                 # calculate the min_confidence level
                 min_confidence_level = MDRCalculator._get_min_confidence_score(dr, all_confidence_scores)
                 
@@ -334,7 +411,7 @@ def calc_metrics_by_profiles(profiles_manager, datasets_manager : DatasetsManage
                     # the remaining node population at the current dr compared to node population at dr = 100
                     info_dict['Node%'] = len(y_true[confidence_mask]) * 100 / len(y_true)
                     # the remaining node population at the current dr compared to the whole population at dr = 100
-                    info_dict['Population%'] = len(y_true) * 100 / len(all_y_true)
+                    info_dict['Population%'] = len(y_true[confidence_mask]) * 100 / len(all_y_true)
                     # the mean confidence level for this profile at this dr
                     info_dict['Mean confidence level'] = np.mean(confidence_scores[confidence_mask]) * 100 if \
                         confidence_scores[confidence_mask].size > 0 else None
diff --git a/MED3pa/med3pa/models.py b/MED3pa/med3pa/models.py
index 17a694d..25eda2a 100644
--- a/MED3pa/med3pa/models.py
+++ b/MED3pa/med3pa/models.py
@@ -4,12 +4,13 @@
 Additionally, it includes Aggregated Predictive Confidence (APC) models that predict uncertainty for groups of similar data points, 
 and Mixed Predictive Confidence (MPC) models that combine the predictions from IPC and APC models.
 """
+import json
 from typing import Any, Dict, List, Optional, Type
 
 import numpy as np
 from sklearn.model_selection import GridSearchCV
 
-from MED3pa.med3pa.tree import TreeRepresentation
+from MED3pa.med3pa.tree import TreeRepresentation, _TreeNode
 from MED3pa.models.abstract_models import RegressionModel
 from MED3pa.models.concrete_regressors import DecisionTreeRegressorModel, RandomForestRegressorModel
 from MED3pa.models.data_strategies import ToDataframesStrategy
@@ -148,6 +149,7 @@ def get_info(self) -> Dict[str, Any]:
             'optimized': self.optimized,
         }
     
+
 class APCModel:
     """
     APCModel class used to predict the Aggregated predicted confidence. ie, the base model confidence for a group of similar data points.
@@ -161,28 +163,44 @@ class APCModel:
             }
     }
 
-    def __init__(self, features: list, params: Optional[Dict[str, Any]] = None) -> None:
+    def __init__(self, features: List[str], params: Optional[Dict[str, Any]] = None, tree_file_path: Optional[str] = None) -> None:
         """
         Initializes the APCModel with the necessary components to perform tree-based regression and to build a tree representation.
 
         Args:
             features (List[str]): List of features used in the model.
-            model_class (Type[RegressionModel]): The regression model class to use for tree-based modeling, default is DecisionTreeRegressorModel.
             params (Optional[Dict[str, Any]]): Parameters to initialize the regression model, default is settings for a basic decision tree.
+            tree_file_path (Optional[str]): Path to the saved tree JSON file, default is None.
         """
         if params is None:
             params = self.default_params
         else:
             random_state_params = {'random_state': 54288}
             params.update(random_state_params)
-            
+        
         self.model = DecisionTreeRegressorModel(params)
         self.treeRepresentation = TreeRepresentation(features=features)
         self.dataPreparationStrategy = ToDataframesStrategy()
         self.features = features
         self.params = params
         self.optimized = False
+        self.loaded_tree = None
+
+        if tree_file_path:
+           self.load_tree(tree_file_path)
 
+    def load_tree(self, file_path: str) -> None:
+        """
+        Loads the tree structure from a JSON file and initializes the tree representation.
+
+        Args:
+            file_path (str): The file path from which the tree structure will be loaded.
+        """
+        with open(file_path, 'r') as file:
+            tree_dict = json.load(file)
+        
+        self.loaded_tree = tree_dict
+        print(self.loaded_tree)
 
     @classmethod
     def supported_models_params(cls) -> Dict[str, Dict[str, Any]]:
@@ -195,7 +213,7 @@ def supported_models_params(cls) -> Dict[str, Dict[str, Any]]:
         """
         return cls.supported_params
     
-    def train(self, x: np.ndarray, error_prob: np.ndarray) -> None:
+    def train(self, x: np.ndarray, error_prob: np.ndarray,) -> None:
         """
         Trains the model using the provided data and error probabilities and builds the tree representation.
 
@@ -205,7 +223,8 @@ def train(self, x: np.ndarray, error_prob: np.ndarray) -> None:
         """
         self.model.train(x, error_prob)
         df_X, df_y, df_w = self.dataPreparationStrategy.execute(column_labels=self.features, observations=x, labels=error_prob)
-        self.treeRepresentation.head = self.treeRepresentation.build_tree(self.model, df_X, error_prob, 0)
+        self.treeRepresentation.head = self.treeRepresentation.build_tree(self.model, df_X, error_prob, 0, loaded_tree=self.loaded_tree)
+        
     
     def optimize(self, param_grid: dict, cv: int, x: np.ndarray, error_prob: np.ndarray, sample_weight: np.ndarray = None) -> None:
         """
@@ -225,10 +244,12 @@ def optimize(self, param_grid: dict, cv: int, x: np.ndarray, error_prob: np.ndar
         self.model.set_model(grid_search.best_estimator_)
         self.model.update_params(grid_search.best_params_)
         self.params.update(grid_search.best_params_)
+        df_X, df_y, df_w = self.dataPreparationStrategy.execute(column_labels=self.features, observations=x, labels=error_prob)
+        self.treeRepresentation.build_tree(self.model, df_X, error_prob, node_id=0)
         self.optimized = True
         
 
-    def predict(self, X: np.ndarray, depth: int = None, min_samples_ratio: float = 0) -> np.ndarray:
+    def predict(self, X: np.ndarray) -> np.ndarray:
         """
         Predicts error probabilities using the tree representation for the given input observations.
 
@@ -244,7 +265,7 @@ def predict(self, X: np.ndarray, depth: int = None, min_samples_ratio: float = 0
 
         for index, row in df_X.iterrows():
             if self.treeRepresentation.head is not None:
-                prediction = self.treeRepresentation.head.assign_node(row, depth, min_samples_ratio)
+                prediction = self.treeRepresentation.head.assign_node(row)
                 predictions.append(prediction)
             else:
                 raise ValueError("The Tree Representation has not been initialized, try fitting the APCModel first.")
diff --git a/MED3pa/med3pa/profiles.py b/MED3pa/med3pa/profiles.py
index 0c09c4a..37404b0 100644
--- a/MED3pa/med3pa/profiles.py
+++ b/MED3pa/med3pa/profiles.py
@@ -7,7 +7,7 @@ class Profile:
     Represents a profile containing metrics and values associated with a specific node.
     """
 
-    def __init__(self, node_id: int, path: list[str], mean_value: float):
+    def __init__(self, node_id: int, path: list[str]):
         """
         Initializes a Profile instance with node details and associated metrics.
 
@@ -19,7 +19,7 @@ def __init__(self, node_id: int, path: list[str], mean_value: float):
         """
         self.node_id = node_id
         self.path = path
-        self.mean_value = mean_value
+        self.mean_value = None
         self.metrics = None
         self.node_information = None
         self.detectron_results = None
@@ -151,7 +151,7 @@ def get_lost_profiles(self, min_samples_ratio=None, dr=None):
         return self.lost_profiles_records
 
     @staticmethod
-    def transform_to_profiles(profiles_list : list[dict], to_dict:bool=True):
+    def transform_to_profiles(profiles_list : list[dict], to_dict:bool=False):
         """
         Transforms a list of profile data into instances of the Profile class or dictionaries.
         
@@ -165,9 +165,9 @@ def transform_to_profiles(profiles_list : list[dict], to_dict:bool=True):
         profiles = []
         for profile in profiles_list:
             if to_dict:
-                profile_ins = Profile(profile['id'], profile['path'], profile['value']).to_dict()
+                profile_ins = Profile(profile['node_id'], profile['path']).to_dict()
             else:
-                profile_ins = Profile(profile['id'], profile['path'], profile['value'])
+                profile_ins = Profile(profile['node_id'], profile['path'])
             profiles.append(profile_ins)
         return profiles
     
diff --git a/MED3pa/med3pa/tree.py b/MED3pa/med3pa/tree.py
index e6a87b6..91ed75e 100644
--- a/MED3pa/med3pa/tree.py
+++ b/MED3pa/med3pa/tree.py
@@ -3,13 +3,40 @@
 and ``TreeNode`` class that represents a node in the tree. 
 This module is crucial for profiling aggregated data and extracting valuable insights
 """
-from typing import Union
+import json
+from typing import Union, Any
 
 from pandas import DataFrame, Series
+import numpy as np
 
 from MED3pa.models.concrete_regressors import DecisionTreeRegressorModel
 from .profiles import Profile
 
+def to_serializable(obj: Any, additional_arg: Any = None) -> Any:
+    """Convert an object to a JSON-serializable format.
+
+    Args:
+        obj (Any): The object to convert.
+
+    Returns:
+        Any: The JSON-serializable representation of the object.
+    """
+    if isinstance(obj, np.ndarray):
+        return obj.tolist()
+    if isinstance(obj, (np.integer, np.floating)):
+        return obj.item()
+    if isinstance(obj, Profile):
+        if additional_arg is not None:
+            return obj.to_dict(additional_arg)
+        else:
+            return obj.to_dict()
+    if isinstance(obj, _TreeNode):
+        return obj.to_dict()
+    if isinstance(obj, dict):
+        return {k: to_serializable(v) for k, v in obj.items()}
+    if isinstance(obj, list):
+        return [to_serializable(v) for v in obj]
+    return obj
 
 class TreeRepresentation:
     """
@@ -26,7 +53,7 @@ def __init__(self, features: list) -> None:
         self.head = None
         self.nb_nodes = 0
 
-    def build_tree(self, dtr: DecisionTreeRegressorModel, X: DataFrame, y: Series, node_id: int = 0, path: list = ['*']) -> '_TreeNode':
+    def build_tree_deprecated(self, dtr: DecisionTreeRegressorModel, X: DataFrame, y: Series, node_id: int = 0, path: list = ['*']) -> '_TreeNode':
         """
         Recursively builds the tree representation starting from the specified node.
 
@@ -74,6 +101,84 @@ def build_tree(self, dtr: DecisionTreeRegressorModel, X: DataFrame, y: Series, n
 
         return curr_node
 
+    def build_tree(self, dtr: DecisionTreeRegressorModel = None, X: DataFrame = None, y: Series = None, 
+               node_id: int = 0, path: list = ['*'], loaded_tree: dict = None) -> '_TreeNode':
+        """
+        Recursively builds the tree representation starting from the specified node, either from a trained decision tree model
+        or from a JSON structure.
+
+        Args:
+            dtr (DecisionTreeRegressorModel, optional): Trained decision tree regressor model.
+            X (DataFrame, optional): Training data observations.
+            y (Series, optional): Training data labels.
+            node_id (int): Node ID to start building from. Defaults to 0.
+            path (Optional[List[str]]): Path to the current node. Defaults to ['*'].
+            json_node (dict, optional): JSON structure representing the tree node.
+
+        Returns:
+            _TreeNode: The root node of the tree representation.
+        """
+        self.nb_nodes += 1
+        # Building tree from JSON structure
+        if loaded_tree is not None:
+            node_feature = loaded_tree.get("feature")
+            node_thresh = loaded_tree.get("threshold")
+            node_feature_id = loaded_tree.get("feature_id")
+
+            if node_feature is not None and node_thresh is not None:
+                left_mask = X[node_feature] <= node_thresh
+                right_mask = X[node_feature] > node_thresh
+
+                curr_node = _TreeNode(
+                    value=y.mean(), value_max=y.max(), samples_ratio=len(y) / len(X) * 100,
+                    threshold=node_thresh, feature=node_feature, feature_id=node_feature_id,
+                    node_id=self.nb_nodes, path=loaded_tree.get("path", [])
+                )
+
+                if "c_left" in loaded_tree:
+                    curr_node.c_left = self.build_tree(X=X[left_mask], y=y[left_mask], loaded_tree=loaded_tree["c_left"])
+                if "c_right" in loaded_tree:
+                    curr_node.c_right = self.build_tree(X=X[right_mask], y=y[right_mask], loaded_tree=loaded_tree["c_right"])
+            else:
+                curr_node = _TreeNode(
+                    value=y.mean(), value_max=y.max(), samples_ratio=len(y) / len(X) * 100,
+                    node_id=self.nb_nodes, path=loaded_tree.get("path", [])
+                )
+            return curr_node 
+        else: 
+            # Building tree from DecisionTreeRegressorModel
+            left_child = dtr.model.tree_.children_left[node_id]
+            right_child = dtr.model.tree_.children_right[node_id]
+
+            node_value = y.mean()
+            node_max = y.max()
+            node_samples_ratio = dtr.model.tree_.n_node_samples[node_id] / dtr.model.tree_.n_node_samples[0] * 100
+
+            # If we are at a leaf
+            if left_child == -1:
+                curr_node = _TreeNode(value=node_value, value_max=node_max, samples_ratio=node_samples_ratio,
+                                    node_id=self.nb_nodes, path=path)
+                return curr_node
+
+            node_thresh = dtr.model.tree_.threshold[node_id]
+            node_feature_id = dtr.model.tree_.feature[node_id]
+            node_feature = self.features[node_feature_id]
+
+            curr_path = list(path)  # Copy the current path to avoid modifying the original list
+            curr_node = _TreeNode(value=node_value, value_max=node_max, samples_ratio=node_samples_ratio,
+                                threshold=node_thresh, feature=node_feature, feature_id=node_feature_id,
+                                node_id=self.nb_nodes, path=curr_path)
+
+            # Update paths for child nodes
+            left_path = curr_path + [f"{node_feature} <= {node_thresh}"]
+            right_path = curr_path + [f"{node_feature} > {node_thresh}"]
+            curr_node.c_left = self.build_tree(dtr, X=X.loc[X[node_feature] <= node_thresh], y=y[X[node_feature] <= node_thresh],
+                                            node_id=left_child, path=left_path)
+            curr_node.c_right = self.build_tree(dtr, X=X.loc[X[node_feature] > node_thresh], y=y[X[node_feature] > node_thresh],
+                                                node_id=right_child, path=right_path)
+
+            return curr_node
+
     def get_all_profiles(self, min_ca: float = 0, min_samples_ratio: float = 0) -> list:
         """
         Retrieves all profiles from the tree that meet the minimum criteria for value and sample ratio.
@@ -90,11 +195,39 @@ def get_all_profiles(self, min_ca: float = 0, min_samples_ratio: float = 0) -> l
         profiles = self.head.get_profile(min_samples_ratio=min_samples_ratio, min_ca=min_ca)
         return profiles
 
+    def get_all_nodes(self) -> list:
+        """
+        Retrieves all nodes from the tree with their paths.
+
+        Returns:
+            List[dict]: A list of dictionaries representing nodes with their paths.
+        """
+        if self.head is None:
+            raise ValueError("Tree has not been built yet.")
+        return self.head.get_all_nodes()
+    
+    def save_tree(self, file_path: str) -> None:
+        """
+        Saves the tree structure to a JSON file.
+
+        Args:
+            file_path (str): The file path where the tree structure will be saved.
+        """
+        if self.head is None:
+            raise ValueError("Tree has not been built yet.")
+        
+        tree_dict = {}
+        tree_dict = self.head.to_dict()
+        tree_dict['features'] = self.features
+        with open(file_path, 'w') as file:
+            json.dump(tree_dict, file, default=to_serializable, indent=4)
+    
+
 class _TreeNode:
     """
     Represents a node in the tree structure.
     """
-    def __init__(self, value: float, value_max: float, samples_ratio: float, threshold: float = None,
+    def __init__(self, value: float =None, value_max: float=None, samples_ratio: float=None, threshold: float = None,
                  feature: str = None, feature_id: int = None, node_id: int = 0, path: list = None) -> None:
         """
         Initializes a _TreeNode object.
@@ -120,7 +253,41 @@ def __init__(self, value: float, value_max: float, samples_ratio: float, thresho
         self.node_id = node_id
         self.path = path if path is not None else []
 
-    def assign_node(self, X: Union[DataFrame, Series], depth: int = None, min_samples_ratio: float = 0) -> float:
+    def assign_node(self, X: Union[DataFrame, Series]) -> float:
+        """
+        Assigns a value to a node based on input observations, navigating the tree until a leaf node is reached.
+
+        Args:
+            X (Union[DataFrame, Series]): Input observations used to navigate and determine the value at a node.
+            min_samples_ratio (float): The minimum sample ratio to consider a node as valid for value assignment. Nodes
+                                    with a sample ratio below this threshold will use the value from the nearest valid ancestor.
+                                    Defaults to 0, which considers all nodes valid regardless of sample ratio.
+
+        Returns:
+            float: The value assigned based on the input observations and the structure of the tree.
+
+        Raises:
+            TypeError: If the input X is neither a pandas DataFrame nor a pandas Series.
+        """
+        # Check if the current node is a leaf node
+        if self.c_left is None and self.c_right is None:
+            return self.value
+
+        if isinstance(X, DataFrame):
+            X_value = X[self.feature].values[0]
+        elif isinstance(X, Series):
+            X_value = X[self.feature]
+        else:
+            raise TypeError(f"Parameter X is of type {type(X)}, but it must be of type 'pandas.DataFrame' or 'pandas.Series'.")
+
+        if X_value <= self.threshold:  # If node split condition is true, then left children
+            c_node = self.c_left
+        else:
+            c_node = self.c_right
+
+        return c_node.assign_node(X)
+
+    def assign_node_deprecated(self, X: Union[DataFrame, Series], depth: int = None, min_samples_ratio: float = 0) -> float:
         """
         Assigns a value to a node based on input observations, potentially navigating the tree up to a certain depth.
 
@@ -155,12 +322,12 @@ def assign_node(self, X: Union[DataFrame, Series], depth: int = None, min_sample
             c_node = self.c_left
         else:
             c_node = self.c_right
-
+        
         if c_node.samples_ratio < min_samples_ratio:  # If not enough samples in child node
             return self.value
-
+        
         return c_node.assign_node(X, depth, min_samples_ratio)
-
+    
     def get_profile(self, min_samples_ratio: float, min_ca: float) -> list:
         """
         Retrieves profiles from the subtree rooted at this node that meet the specified criteria.
@@ -182,14 +349,50 @@ def get_profile(self, min_samples_ratio: float, min_ca: float) -> list:
             # Recursively retrieve profiles from the right child
             profiles.extend(self.c_right.get_profile(min_samples_ratio, min_ca))
 
-        if min_samples_ratio < 0:  # Case where we don't use profiles
-            if self.value_max < min_ca and len(profiles) == 0:
-                return []
-        else:
-            if self.value < min_ca and len(profiles) == 0:
-                return []
+        # Check if the current node meets the criteria
+        if self.samples_ratio >= min_samples_ratio and self.value >= min_ca:
+            profile = Profile(node_id=self.node_id, path=self.path)
+            profiles.append(profile)
 
-        profile = Profile(node_id=self.node_id, path=self.path, mean_value=self.value)
-        return profiles + [profile]
+        return profiles
 
+    def get_all_nodes(self) -> list:
+        """
+        Retrieves all nodes in the subtree rooted at this node with their paths.
 
+        Returns:
+            List[dict]: A list of dictionaries representing nodes with their paths.
+        """
+        nodes = [{
+            'node_id': self.node_id,
+            'path': self.path
+        }]
+        
+        if self.c_left is not None:
+            nodes.extend(self.c_left.get_all_nodes())
+        
+        if self.c_right is not None:
+            nodes.extend(self.c_right.get_all_nodes())
+        
+        return nodes
+    
+    def to_dict(self) -> dict:
+        """
+        Converts the node and its children to a dictionary.
+
+        Returns:
+            dict: A dictionary representation of the node and its children.
+        """
+        node_dict = {
+            'threshold': self.threshold,
+            'feature': self.feature,
+            'feature_id': self.feature_id,
+            'node_id': self.node_id,
+            'path': self.path
+        }
+        if self.c_left is not None:
+            node_dict['c_left'] = self.c_left.to_dict()
+        if self.c_right is not None:
+            node_dict['c_right'] = self.c_right.to_dict()
+        return node_dict
+    
diff --git a/MED3pa/models/classification_metrics.py b/MED3pa/models/classification_metrics.py
index 30da143..7f5f64d 100644
--- a/MED3pa/models/classification_metrics.py
+++ b/MED3pa/models/classification_metrics.py
@@ -47,7 +47,7 @@ def recall(y_true: np.ndarray, y_pred: np.ndarray, sample_weight: np.ndarray = N
         Returns:
             float: Recall score.
         """
-        if y_true.size == 0 or y_pred.size == 0:
+        if y_true.size == 0 or y_pred.size == 0 or len(np.unique(y_true)) == 1:
             return None
         with warnings.catch_warnings():
             warnings.simplefilter("ignore")
@@ -66,7 +66,7 @@ def roc_auc(y_true: np.ndarray, y_pred: np.ndarray, sample_weight: np.ndarray =
         Returns:
             float: ROC AUC score.
         """
-        if y_true.size == 0 or y_pred.size == 0 or len(np.unique(y_true))==1:
+        if y_true.size == 0 or y_pred.size == 0 or len(np.unique(y_true)) == 1:
             return None
         with warnings.catch_warnings():
             warnings.simplefilter("ignore")
@@ -85,7 +85,7 @@ def average_precision(y_true: np.ndarray, y_pred: np.ndarray, sample_weight: np.
         Returns:
             float: Average precision score.
         """
-        if y_true.size == 0 or y_pred.size == 0 or len(np.unique(y_true))==1:
+        if y_true.size == 0 or y_pred.size == 0 or len(np.unique(y_true)) == 1:
             return None
         with warnings.catch_warnings():
             warnings.simplefilter("ignore")
@@ -104,11 +104,11 @@ def matthews_corrcoef(y_true: np.ndarray, y_pred: np.ndarray, sample_weight: np.
         Returns:
             float: Matthews correlation coefficient.
         """
-        if y_true.size == 0 or y_pred.size == 0:
+        if y_true.size == 0 or y_pred.size == 0 or len(np.unique(y_true)) == 1:
             return None
         with warnings.catch_warnings():
             warnings.simplefilter("ignore")
-            return matthews_corrcoef(y_true, y_pred, sample_weight=sample_weight)
+            return matthews_corrcoef(y_true, y_pred)
     
     @staticmethod
     def precision(y_true: np.ndarray, y_pred: np.ndarray, sample_weight: np.ndarray = None) -> Optional[float]:
@@ -123,7 +123,7 @@ def precision(y_true: np.ndarray, y_pred: np.ndarray, sample_weight: np.ndarray
         Returns:
             float: Precision score.
         """
-        if y_true.size == 0 or y_pred.size == 0:
+        if y_true.size == 0 or y_pred.size == 0 or len(np.unique(y_true)) == 1:
             return None
         with warnings.catch_warnings():
             warnings.simplefilter("ignore")
@@ -142,7 +142,7 @@ def f1_score(y_true: np.ndarray, y_pred: np.ndarray, sample_weight: np.ndarray =
         Returns:
             float: F1 score.
         """
-        if y_true.size == 0 or y_pred.size == 0:
+        if y_true.size == 0 or y_pred.size == 0 or len(np.unique(y_true)) == 1:
             return None
         with warnings.catch_warnings():
             warnings.simplefilter("ignore")
@@ -161,11 +161,11 @@ def sensitivity(y_true: np.ndarray, y_pred: np.ndarray, sample_weight: np.ndarra
         Returns:
             float: Sensitivity score.
         """
-        if y_true.size == 0 or y_pred.size == 0:
+        if y_true.size == 0 or y_pred.size == 0 or len(np.unique(y_true)) == 1:
             return None
         with warnings.catch_warnings():
             warnings.simplefilter("ignore")
-            return recall_score(y_true, y_pred, pos_label=1, zero_division=0)
+            return recall_score(y_true, y_pred, pos_label=1, sample_weight=sample_weight, zero_division=0)
     
     @staticmethod
     def specificity(y_true: np.ndarray, y_pred: np.ndarray, sample_weight: np.ndarray = None) -> Optional[float]:
@@ -180,11 +180,11 @@ def specificity(y_true: np.ndarray, y_pred: np.ndarray, sample_weight: np.ndarra
         Returns:
             float: Specificity score.
         """
-        if y_true.size == 0 or y_pred.size == 0:
+        if y_true.size == 0 or y_pred.size == 0 or len(np.unique(y_true)) == 1:
             return None
         with warnings.catch_warnings():
             warnings.simplefilter("ignore")
-            return recall_score(y_true, y_pred, pos_label=0, zero_division=0)
+            return recall_score(y_true, y_pred, pos_label=0, sample_weight=sample_weight, zero_division=0)
     
     @staticmethod
     def ppv(y_true: np.ndarray, y_pred: np.ndarray, sample_weight: np.ndarray = None) -> Optional[float]:
@@ -203,7 +203,7 @@ def ppv(y_true: np.ndarray, y_pred: np.ndarray, sample_weight: np.ndarray = None
             return None
         with warnings.catch_warnings():
             warnings.simplefilter("ignore")
-            return precision_score(y_true, y_pred, pos_label=1, zero_division=0)
+            return precision_score(y_true, y_pred, pos_label=1, sample_weight=sample_weight, zero_division=0)
     
     @staticmethod
     def npv(y_true: np.ndarray, y_pred: np.ndarray, sample_weight: np.ndarray = None) -> Optional[float]:
@@ -222,7 +222,7 @@ def npv(y_true: np.ndarray, y_pred: np.ndarray, sample_weight: np.ndarray = None
             return None
         with warnings.catch_warnings():
             warnings.simplefilter("ignore")
-            return precision_score(y_true, y_pred, pos_label=0, zero_division=0)
+            return precision_score(y_true, y_pred, pos_label=0, sample_weight=sample_weight, zero_division=0)
     
     @staticmethod
     def balanced_accuracy(y_true: np.ndarray, y_pred: np.ndarray, sample_weight: np.ndarray = None) -> Optional[float]:
@@ -237,7 +237,7 @@ def balanced_accuracy(y_true: np.ndarray, y_pred: np.ndarray, sample_weight: np.
         Returns:
             float: Balanced accuracy score.
         """
-        if y_true.size == 0 or y_pred.size == 0:
+        if y_true.size == 0 or y_pred.size == 0 or len(np.unique(y_true)) == 1:
             return None
         sens = ClassificationEvaluationMetrics.sensitivity(y_true, y_pred)
         spec = ClassificationEvaluationMetrics.specificity(y_true, y_pred)
@@ -259,7 +259,7 @@ def log_loss(y_true: np.ndarray, y_pred: np.ndarray, sample_weight: np.ndarray =
         Returns:
             float: Log loss score.
         """
-        if y_true.size == 0 or y_pred.size == 0 or len(np.unique(y_true))==1:
+        if y_true.size == 0 or y_pred.size == 0 or len(np.unique(y_true)) == 1:
             return None
         y_pred = np.clip(y_pred, 1e-15, 1 - 1e-15)
         with warnings.catch_warnings():
diff --git a/setup.py b/setup.py
index a2bafeb..d8879a9 100644
--- a/setup.py
+++ b/setup.py
@@ -9,7 +9,7 @@
 
 setup(
     name="MED3pa",
-    version="0.1.9",
+    version="0.1.10",
     author="MEDomics consortium",
     author_email="medomics.info@gmail.com",
     description="Python Open-source package for ensuring robust and reliable ML models deployments",
diff --git a/tutorials/data/test_data_shifted_0.1.csv b/tutorials/data/test_data_shifted_0.1.csv
new file mode 100644
index 0000000..bc6659e
--- /dev/null
+++ b/tutorials/data/test_data_shifted_0.1.csv
@@ -0,0 +1,117 @@
+Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome
+0.08397217383281642,0.5053403788110761,0.7918515691365485,-1.3202153967120496,-0.6936877995463384,-0.0893849148174294,-0.796462856076008,-0.2633522074251788,0
+-0.5295388104967399,-1.2321228263227832,-0.5357642787926612,0.10131437878523027,-0.6936877995463384,-0.5633300353493,-0.4047033031504579,-0.6916385047822984,0
+0.39072766599759456,0.8124172449906046,0.4599476071542461,-1.3202153967120496,-0.6936877995463384,0.23443752231245715,-1.0754003252265774,2.734651874074658,0
+1.0042386503271508,1.1877326770773375,-0.3144949708044596,-1.3202153967120496,-0.6936877995463384,-0.5885720473510628,-0.28615878513695564,0.5932203872890603,0
+-0.8362943026615179,0.1342517691371132,-1.0889375487631652,1.2643841951011865,0.7573578287312339,0.17880623371891888,1.8770839260366596,-0.4346667263680266,1
+2.2312606189862634,-1.1125831017282362,0.2386782991660445,-1.3202153967120496,-0.6936877995463384,-0.260659429518831,-1.1028465465493127,0.16493408993194075,0
+1.0042386503271508,1.9575062218550452,0.7918515691365485,0.8120792665338702,-0.6936877995463384,0.46659252583190763,-0.21152918015557914,0.6788776467604842,1
+-1.143049794826296,-0.5838641117453991,0.2939956261630949,0.16592936858056118,-0.6936877995463384,-4.180297242729609,0.49674203377035764,-1.034267542667994,0
+-0.5295388104967399,-1.1408285710009671,-3.8548038986156854,-1.3202153967120496,-0.6936877995463384,-4.206828022713983,-0.6719855551159051,-1.034267542667994,0
+1.9245051268214854,-0.1797651021693601,-3.8548038986156854,-1.3202153967120496,-0.6936877995463384,-4.199674219240085,-0.7275303694993721,-0.2633522074251788,1
+-1.143049794826296,0.09796661593384746,-0.7570335867808629,-0.4802205293727479,0.21864627811321305,-1.3415509315712946,-0.09131317021661119,-1.034267542667994,0
+-1.143049794826296,0.5972338319995321,-0.25917764380740915,0.3597743379665539,0.43586867755596337,1.419031586106332,-0.2010102560824739,-0.7772957642537223,1
+1.6177496346567073,-0.45723719211897523,-0.9783028947690644,-1.3202153967120496,-0.6936877995463384,-0.09299451213783234,-0.3535175199433205,0.7645349062319081,0
+0.6974831581623727,-0.5211175620435649,0.2386782991660445,-0.15714558039609336,0.6617799729764238,-0.26713326352330524,0.2471838246605734,0.6788776467604842,1
+-1.143049794826296,-0.5820218600006508,0.9024862231306494,-0.22176057019142426,0.21864627811321305,-0.3613568119881684,0.9957413690123965,-0.5203239858394505,0
+2.8447716033158197,1.1508789448646448,2.451371379048061,-1.3202153967120496,-0.6936877995463384,1.3624503570443502,-0.3071026311205042,0.935849425174756,1
+0.39072766599759456,-1.1049340672786734,0.2386782991660445,0.10131437878523027,-0.6936877995463384,-0.3994855604501441,1.9684179769240002,-0.09203768848233097,1
+-1.143049794826296,1.2730142068549368,0.3493129531601453,2.298224031826481,0.17520179822466297,2.786724810154808,0.4167662008032547,-0.6916385047822984,1
+-1.143049794826296,-0.05626397815871477,-3.8548038986156854,-1.3202153967120496,-0.6936877995463384,0.0473666229050971,-0.8099438003041193,-0.7772957642537223,1
+-1.143049794826296,-0.6479406345761194,0.017408991177842868,0.3597743379665539,-0.2592430006608377,-0.14545158640854441,0.2033576450026222,-1.034267542667994,0
+-0.22278331833196172,-1.272213784327345,0.6812169151424478,0.6828492869432085,-0.08546508110663745,0.29958463363688087,2.4469045771809688,-0.5203239858394505,1
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diff --git a/tutorials/data/test_data_shifted_1.6.csv b/tutorials/data/test_data_shifted_1.6.csv
new file mode 100644
index 0000000..776274c
--- /dev/null
+++ b/tutorials/data/test_data_shifted_1.6.csv
@@ -0,0 +1,117 @@
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diff --git a/tutorials/detectron_experiment_results/detectron_results.json b/tutorials/detectron_experiment_results/detectron_results.json
deleted file mode 100644
index 8791219..0000000
--- a/tutorials/detectron_experiment_results/detectron_results.json
+++ /dev/null
@@ -1,22 +0,0 @@
-[
-    {
-        "shift_probability": 0.8111111111111111,
-        "test_statistic": 8.466666666666667,
-        "baseline_mean": 7.4,
-        "baseline_std": 1.2631530214330944,
-        "significance_description": {
-            "no shift": 38.34567901234568,
-            "small": 15.592592592592592,
-            "moderate": 16.34567901234568,
-            "large": 29.716049382716047
-        },
-        "Strategy": "enhanced_disagreement_strategy"
-    },
-    {
-        "p_value": 0.00016360887668277182,
-        "u_statistic": 3545.0,
-        "z-score": 0.4685784328619402,
-        "shift significance": "Small",
-        "Strategy": "mannwhitney_strategy"
-    }
-]
\ No newline at end of file
diff --git a/tutorials/detectron_experiment_results/experiment_config.json b/tutorials/detectron_experiment_results/experiment_config.json
deleted file mode 100644
index 42bb09e..0000000
--- a/tutorials/detectron_experiment_results/experiment_config.json
+++ /dev/null
@@ -1,117 +0,0 @@
-{
-    "experiment_name": "DetectronExperiment",
-    "datasets": {
-        "training_set": {
-            "num_samples": 537,
-            "num_observations": 8,
-            "has_pseudo_labels": false,
-            "has_pseudo_probabilities": false,
-            "has_confidence_scores": false
-        },
-        "validation_set": {
-            "num_samples": 115,
-            "num_observations": 8,
-            "has_pseudo_labels": false,
-            "has_pseudo_probabilities": false,
-            "has_confidence_scores": false
-        },
-        "reference_set": {
-            "num_samples": 116,
-            "num_observations": 8,
-            "has_pseudo_labels": false,
-            "has_pseudo_probabilities": false,
-            "has_confidence_scores": false
-        },
-        "testing_set": {
-            "num_samples": 116,
-            "num_observations": 8,
-            "has_pseudo_labels": false,
-            "has_pseudo_probabilities": false,
-            "has_confidence_scores": false
-        },
-        "column_labels": [
-            "Pregnancies",
-            "Glucose",
-            "BloodPressure",
-            "SkinThickness",
-            "Insulin",
-            "BMI",
-            "DiabetesPedigreeFunction",
-            "Age"
-        ]
-    },
-    "base_model": {
-        "model": "XGBoostModel",
-        "model_type": "Booster",
-        "params": {
-            "device": "cpu",
-            "fail_on_invalid_gpu_id": 0,
-            "n_jobs": 0,
-            "nthread": 0,
-            "random_state": 0,
-            "seed": 10,
-            "seed_per_iteration": 0,
-            "validate_parameters": 1,
-            "process_type": "default",
-            "tree_method": "auto",
-            "updater": "grow_quantile_histmaker",
-            "updater_seq": "grow_quantile_histmaker",
-            "alpha": 0,
-            "cache_opt": 1,
-            "colsample_bylevel": 1,
-            "colsample_bynode": 1,
-            "colsample_bytree": 0.824717641,
-            "eta": 0.0710294247,
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-            "grow_policy": "depthwise",
-            "interaction_constraints": "",
-            "lambda": 1,
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-            "max_bin": 256,
-            "max_cat_threshold": 64,
-            "max_cat_to_onehot": 4,
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-            "max_depth": 9,
-            "max_leaves": 0,
-            "min_child_weight": 1,
-            "min_split_loss": 0.302559406,
-            "monotone_constraints": "()",
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-            "objective": "reg:squarederror",
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-            "num_feature": 8,
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-            ],
-            "num_boost_rounds": 30
-        },
-        "data_preparation_strategy": "ToDmatrixStrategy",
-        "pickled_model": true
-    },
-    "additional_training_params": null,
-    "samples_size": 20,
-    "cdcs_ensemble_size": 10,
-    "num_runs": 100,
-    "patience": 3,
-    "allow_margin": false,
-    "margin": 0.05,
-    "test_strategies": [
-        "enhanced_disagreement_strategy",
-        "mannwhitney_strategy"
-    ]
-}
\ No newline at end of file
diff --git a/tutorials/med3pa_detectron_experiment_results/detectron/detectron_results.json b/tutorials/med3pa_detectron_experiment_results/detectron/detectron_results.json
deleted file mode 100644
index 146931b..0000000
--- a/tutorials/med3pa_detectron_experiment_results/detectron/detectron_results.json
+++ /dev/null
@@ -1,15 +0,0 @@
-[
-    {
-        "shift_probability": 0.8111111111111111,
-        "test_statistic": 8.466666666666667,
-        "baseline_mean": 7.4,
-        "baseline_std": 1.2631530214330944,
-        "significance_description": {
-            "no shift": 38.34567901234568,
-            "small": 15.592592592592592,
-            "moderate": 16.34567901234568,
-            "large": 29.716049382716047
-        },
-        "Strategy": "enhanced_disagreement_strategy"
-    }
-]
\ No newline at end of file
diff --git a/tutorials/med3pa_detectron_experiment_results/experiment_config.json b/tutorials/med3pa_detectron_experiment_results/experiment_config.json
deleted file mode 100644
index a9f8a2e..0000000
--- a/tutorials/med3pa_detectron_experiment_results/experiment_config.json
+++ /dev/null
@@ -1,150 +0,0 @@
-{
-    "experiment_name": "Med3paDetectronExperiment",
-    "datasets": {
-        "training_set": {
-            "file_path": "./data/train_data.csv",
-            "num_samples": 537,
-            "num_observations": 8,
-            "has_pseudo_labels": false,
-            "has_pseudo_probabilities": false,
-            "has_confidence_scores": false
-        },
-        "validation_set": {
-            "file_path": "./data/val_data.csv",
-            "num_samples": 115,
-            "num_observations": 8,
-            "has_pseudo_labels": false,
-            "has_pseudo_probabilities": false,
-            "has_confidence_scores": false
-        },
-        "reference_set": {
-            "file_path": "./data/test_data.csv",
-            "num_samples": 116,
-            "num_observations": 8,
-            "has_pseudo_labels": true,
-            "has_pseudo_probabilities": true,
-            "has_confidence_scores": true
-        },
-        "testing_set": {
-            "file_path": "./data/test_data_shifted_0.6.csv",
-            "num_samples": 116,
-            "num_observations": 8,
-            "has_pseudo_labels": true,
-            "has_pseudo_probabilities": true,
-            "has_confidence_scores": true
-        },
-        "column_labels": [
-            "Pregnancies",
-            "Glucose",
-            "BloodPressure",
-            "SkinThickness",
-            "Insulin",
-            "BMI",
-            "DiabetesPedigreeFunction",
-            "Age"
-        ]
-    },
-    "base_model": {
-        "model": "XGBoostModel",
-        "model_type": "Booster",
-        "params": {
-            "device": "cpu",
-            "fail_on_invalid_gpu_id": 0,
-            "n_jobs": 0,
-            "nthread": 0,
-            "random_state": 0,
-            "seed": 5,
-            "seed_per_iteration": 0,
-            "validate_parameters": 1,
-            "process_type": "default",
-            "tree_method": "auto",
-            "updater": "grow_quantile_histmaker",
-            "updater_seq": "grow_quantile_histmaker",
-            "alpha": 0,
-            "cache_opt": 1,
-            "colsample_bylevel": 1,
-            "colsample_bynode": 1,
-            "colsample_bytree": 0.824717641,
-            "eta": 0.0710294247,
-            "gamma": 0.302559406,
-            "grow_policy": "depthwise",
-            "interaction_constraints": "",
-            "lambda": 1,
-            "learning_rate": 0.0710294247,
-            "max_bin": 256,
-            "max_cat_threshold": 64,
-            "max_cat_to_onehot": 4,
-            "max_delta_step": 0,
-            "max_depth": 9,
-            "max_leaves": 0,
-            "min_child_weight": 1,
-            "min_split_loss": 0.302559406,
-            "monotone_constraints": "()",
-            "refresh_leaf": 1,
-            "reg_alpha": 0,
-            "reg_lambda": 1,
-            "sampling_method": "uniform",
-            "sketch_ratio": 2,
-            "sparse_threshold": 0.2,
-            "subsample": 0.817121327,
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-            "max_cached_hist_node": 65536,
-            "booster": "gbtree",
-            "disable_default_eval_metric": 0,
-            "multi_strategy": "one_output_per_tree",
-            "objective": "reg:squarederror",
-            "scale_pos_weight": 1,
-            "base_score": 0.3500931,
-            "boost_from_average": 1,
-            "num_class": 0,
-            "num_feature": 8,
-            "num_target": 1,
-            "eval_metric": [
-                "auc"
-            ],
-            "num_boost_rounds": 30
-        },
-        "data_preparation_strategy": "ToDmatrixStrategy",
-        "pickled_model": true,
-        "file_path": "./models/diabetes_xgb_model.pkl"
-    },
-    "uncertainty_metric": "absolute_error",
-    "ipc_model": {
-        "model_name": "RandomForestRegressor",
-        "params": {
-            "n_estimators": 100,
-            "random_state": 54288
-        },
-        "optimized": false
-    },
-    "apc_model": {
-        "model_name": "DecisionTreeRegressor",
-        "params": {
-            "max_depth": 3,
-            "random_state": 54288
-        },
-        "optimized": false
-    },
-    "samples_ratio_min": 0,
-    "samples_ratio_max": 10,
-    "samples_ratio_step": 5,
-    "med3pa_metrics": [
-        "Auc",
-        "Accuracy",
-        "BalancedAccuracy"
-    ],
-    "evaluate_models": true,
-    "models_evaluation_metrics": [
-        "MSE",
-        "RMSE"
-    ],
-    "mode": "mpc",
-    "additional_training_params": null,
-    "samples_size": 20,
-    "profiles_samples_size": 10,
-    "cdcs_ensemble_size": 10,
-    "num_runs": 100,
-    "patience": 3,
-    "allow_margin": false,
-    "margin": 0.05
-}
\ No newline at end of file
diff --git a/tutorials/med3pa_detectron_experiment_results/reference/dataset_0.csv b/tutorials/med3pa_detectron_experiment_results/reference/dataset_0.csv
deleted file mode 100644
index 1260e87..0000000
--- a/tutorials/med3pa_detectron_experiment_results/reference/dataset_0.csv
+++ /dev/null
@@ -1,117 +0,0 @@
-Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,true_labels,pseudo_labels,pseudo_probabilities,confidence_scores
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diff --git a/tutorials/med3pa_detectron_experiment_results/reference/dataset_10.csv b/tutorials/med3pa_detectron_experiment_results/reference/dataset_10.csv
deleted file mode 100644
index 8c6ecfa..0000000
--- a/tutorials/med3pa_detectron_experiment_results/reference/dataset_10.csv
+++ /dev/null
@@ -1,117 +0,0 @@
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diff --git a/tutorials/med3pa_detectron_experiment_results/reference/dataset_5.csv b/tutorials/med3pa_detectron_experiment_results/reference/dataset_5.csv
deleted file mode 100644
index 9ecc0bc..0000000
--- a/tutorials/med3pa_detectron_experiment_results/reference/dataset_5.csv
+++ /dev/null
@@ -1,117 +0,0 @@
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diff --git a/tutorials/med3pa_detectron_experiment_results/reference/lost_profiles.json b/tutorials/med3pa_detectron_experiment_results/reference/lost_profiles.json
deleted file mode 100644
index 34a72c5..0000000
--- a/tutorials/med3pa_detectron_experiment_results/reference/lost_profiles.json
+++ /dev/null
@@ -1,7653 +0,0 @@
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diff --git a/tutorials/med3pa_detectron_experiment_results/reference/models_evaluation.json b/tutorials/med3pa_detectron_experiment_results/reference/models_evaluation.json
deleted file mode 100644
index be5c8d2..0000000
--- a/tutorials/med3pa_detectron_experiment_results/reference/models_evaluation.json
+++ /dev/null
@@ -1,10 +0,0 @@
-{
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\ No newline at end of file
diff --git a/tutorials/med3pa_detectron_experiment_results/reference/profiles.json b/tutorials/med3pa_detectron_experiment_results/reference/profiles.json
deleted file mode 100644
index c5da6ca..0000000
--- a/tutorials/med3pa_detectron_experiment_results/reference/profiles.json
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index 0a01388..0000000
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diff --git a/tutorials/med3pa_detectron_experiment_results/test/dataset_10.csv b/tutorials/med3pa_detectron_experiment_results/test/dataset_10.csv
deleted file mode 100644
index 0b3fdf5..0000000
--- a/tutorials/med3pa_detectron_experiment_results/test/dataset_10.csv
+++ /dev/null
@@ -1,117 +0,0 @@
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diff --git a/tutorials/med3pa_detectron_experiment_results/test/dataset_5.csv b/tutorials/med3pa_detectron_experiment_results/test/dataset_5.csv
deleted file mode 100644
index b26e458..0000000
--- a/tutorials/med3pa_detectron_experiment_results/test/dataset_5.csv
+++ /dev/null
@@ -1,117 +0,0 @@
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diff --git a/tutorials/med3pa_detectron_experiment_results/test/lost_profiles.json b/tutorials/med3pa_detectron_experiment_results/test/lost_profiles.json
deleted file mode 100644
index b8234cc..0000000
--- a/tutorials/med3pa_detectron_experiment_results/test/lost_profiles.json
+++ /dev/null
@@ -1,7923 +0,0 @@
-{
-    "0": {
-        "100": [],
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-        "98": [],
-        "97": [],
-        "96": [],
-        "95": [
-            {
-                "id": 8,
-                "path": [
-                    "*",
-                    "Age <= -0.8201243877410889",
-                    "BloodPressure > 0.4599476158618927",
-                    "Pregnancies > 0.6974831596016884"
-                ]
-            }
-        ],
-        "94": [
-            {
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-                "path": [
-                    "*",
-                    "Age <= -0.8201243877410889",
-                    "BloodPressure > 0.4599476158618927",
-                    "Pregnancies > 0.6974831596016884"
-                ]
-            }
-        ],
-        "93": [
-            {
-                "id": 8,
-                "path": [
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diff --git a/tutorials/med3pa_detectron_experiment_results/test/models_evaluation.json b/tutorials/med3pa_detectron_experiment_results/test/models_evaluation.json
deleted file mode 100644
index 3a210d1..0000000
--- a/tutorials/med3pa_detectron_experiment_results/test/models_evaluation.json
+++ /dev/null
@@ -1,10 +0,0 @@
-{
-    "IPC_evaluation": {
-        "MSE": 0.019718520229182943,
-        "RMSE": 0.1404226485620569
-    },
-    "APC_evaluation": {
-        "MSE": 0.024760989627306784,
-        "RMSE": 0.1573562506775844
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-}
\ No newline at end of file
diff --git a/tutorials/med3pa_detectron_experiment_results/test/profiles.json b/tutorials/med3pa_detectron_experiment_results/test/profiles.json
deleted file mode 100644
index b300958..0000000
--- a/tutorials/med3pa_detectron_experiment_results/test/profiles.json
+++ /dev/null
@@ -1,53898 +0,0 @@
-{
-    "0": {
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-                    "Age <= -0.8201243877410889",
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-                "id": 8,
-                "path": [
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diff --git a/tutorials/med3pa_experiment_results/experiment_config.json b/tutorials/med3pa_experiment_results/experiment_config.json
deleted file mode 100644
index ffa9e24..0000000
--- a/tutorials/med3pa_experiment_results/experiment_config.json
+++ /dev/null
@@ -1,128 +0,0 @@
-{
-    "experiment_name": "Med3paExperiment",
-    "datasets": {
-        "training_set": "Not set",
-        "validation_set": "Not set",
-        "reference_set": {
-            "file_path": "./data/test_data.csv",
-            "num_samples": 116,
-            "num_observations": 8,
-            "has_pseudo_labels": true,
-            "has_pseudo_probabilities": true,
-            "has_confidence_scores": true
-        },
-        "testing_set": {
-            "file_path": "./data/test_data_shifted_0.6.csv",
-            "num_samples": 116,
-            "num_observations": 8,
-            "has_pseudo_labels": true,
-            "has_pseudo_probabilities": true,
-            "has_confidence_scores": true
-        },
-        "column_labels": [
-            "Pregnancies",
-            "Glucose",
-            "BloodPressure",
-            "SkinThickness",
-            "Insulin",
-            "BMI",
-            "DiabetesPedigreeFunction",
-            "Age"
-        ]
-    },
-    "base_model": {
-        "model": "XGBoostModel",
-        "model_type": "Booster",
-        "params": {
-            "device": "cpu",
-            "fail_on_invalid_gpu_id": 0,
-            "n_jobs": 0,
-            "nthread": 0,
-            "random_state": 0,
-            "seed": 0,
-            "seed_per_iteration": 0,
-            "validate_parameters": 1,
-            "process_type": "default",
-            "tree_method": "auto",
-            "updater": "grow_quantile_histmaker",
-            "updater_seq": "grow_quantile_histmaker",
-            "alpha": 0,
-            "cache_opt": 1,
-            "colsample_bylevel": 1,
-            "colsample_bynode": 1,
-            "colsample_bytree": 0.824717641,
-            "eta": 0.0710294247,
-            "gamma": 0.302559406,
-            "grow_policy": "depthwise",
-            "interaction_constraints": "",
-            "lambda": 1,
-            "learning_rate": 0.0710294247,
-            "max_bin": 256,
-            "max_cat_threshold": 64,
-            "max_cat_to_onehot": 4,
-            "max_delta_step": 0,
-            "max_depth": 9,
-            "max_leaves": 0,
-            "min_child_weight": 1,
-            "min_split_loss": 0.302559406,
-            "monotone_constraints": "()",
-            "refresh_leaf": 1,
-            "reg_alpha": 0,
-            "reg_lambda": 1,
-            "sampling_method": "uniform",
-            "sketch_ratio": 2,
-            "sparse_threshold": 0.2,
-            "subsample": 0.817121327,
-            "debug_synchronize": 0,
-            "max_cached_hist_node": 65536,
-            "booster": "gbtree",
-            "disable_default_eval_metric": 0,
-            "multi_strategy": "one_output_per_tree",
-            "objective": "reg:squarederror",
-            "scale_pos_weight": 1,
-            "base_score": 0.3500931,
-            "boost_from_average": 1,
-            "num_class": 0,
-            "num_feature": 8,
-            "num_target": 1,
-            "eval_metric": [
-                "auc"
-            ],
-            "num_boost_rounds": 30
-        },
-        "data_preparation_strategy": "ToDmatrixStrategy",
-        "pickled_model": true,
-        "file_path": "./models/diabetes_xgb_model.pkl"
-    },
-    "uncertainty_metric": "absolute_error",
-    "ipc_model": {
-        "model_name": "RandomForestRegressor",
-        "params": {
-            "n_estimators": 100,
-            "random_state": 54288
-        },
-        "optimized": false
-    },
-    "apc_model": {
-        "model_name": "DecisionTreeRegressor",
-        "params": {
-            "max_depth": 3,
-            "random_state": 54288
-        },
-        "optimized": false
-    },
-    "samples_ratio_min": 0,
-    "samples_ratio_max": 10,
-    "samples_ratio_step": 5,
-    "med3pa_metrics": [
-        "Auc",
-        "Accuracy",
-        "BalancedAccuracy"
-    ],
-    "evaluate_models": true,
-    "models_evaluation_metrics": [
-        "MSE",
-        "RMSE"
-    ],
-    "mode": "mpc"
-}
\ No newline at end of file
diff --git a/tutorials/med3pa_experiment_results/reference/dataset_0.csv b/tutorials/med3pa_experiment_results/reference/dataset_0.csv
deleted file mode 100644
index 1260e87..0000000
--- a/tutorials/med3pa_experiment_results/reference/dataset_0.csv
+++ /dev/null
@@ -1,117 +0,0 @@
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diff --git a/tutorials/med3pa_experiment_results/reference/dataset_10.csv b/tutorials/med3pa_experiment_results/reference/dataset_10.csv
deleted file mode 100644
index 8c6ecfa..0000000
--- a/tutorials/med3pa_experiment_results/reference/dataset_10.csv
+++ /dev/null
@@ -1,117 +0,0 @@
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diff --git a/tutorials/med3pa_experiment_results/reference/dataset_5.csv b/tutorials/med3pa_experiment_results/reference/dataset_5.csv
deleted file mode 100644
index 9ecc0bc..0000000
--- a/tutorials/med3pa_experiment_results/reference/dataset_5.csv
+++ /dev/null
@@ -1,117 +0,0 @@
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diff --git a/tutorials/med3pa_experiment_results/reference/lost_profiles.json b/tutorials/med3pa_experiment_results/reference/lost_profiles.json
deleted file mode 100644
index 34a72c5..0000000
--- a/tutorials/med3pa_experiment_results/reference/lost_profiles.json
+++ /dev/null
@@ -1,7653 +0,0 @@
-{
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index 0332e2b..0000000
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diff --git a/tutorials/med3pa_experiment_results/reference/models_evaluation.json b/tutorials/med3pa_experiment_results/reference/models_evaluation.json
deleted file mode 100644
index be5c8d2..0000000
--- a/tutorials/med3pa_experiment_results/reference/models_evaluation.json
+++ /dev/null
@@ -1,10 +0,0 @@
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\ No newline at end of file
diff --git a/tutorials/med3pa_experiment_results/reference/profiles.json b/tutorials/med3pa_experiment_results/reference/profiles.json
deleted file mode 100644
index c5da6ca..0000000
--- a/tutorials/med3pa_experiment_results/reference/profiles.json
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diff --git a/tutorials/med3pa_experiment_results/test/dataset_0.csv b/tutorials/med3pa_experiment_results/test/dataset_0.csv
deleted file mode 100644
index 0a01388..0000000
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diff --git a/tutorials/med3pa_experiment_results/test/dataset_10.csv b/tutorials/med3pa_experiment_results/test/dataset_10.csv
deleted file mode 100644
index 0b3fdf5..0000000
--- a/tutorials/med3pa_experiment_results/test/dataset_10.csv
+++ /dev/null
@@ -1,117 +0,0 @@
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diff --git a/tutorials/med3pa_experiment_results/test/dataset_5.csv b/tutorials/med3pa_experiment_results/test/dataset_5.csv
deleted file mode 100644
index b26e458..0000000
--- a/tutorials/med3pa_experiment_results/test/dataset_5.csv
+++ /dev/null
@@ -1,117 +0,0 @@
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diff --git a/tutorials/med3pa_experiment_results/test/lost_profiles.json b/tutorials/med3pa_experiment_results/test/lost_profiles.json
deleted file mode 100644
index b8234cc..0000000
--- a/tutorials/med3pa_experiment_results/test/lost_profiles.json
+++ /dev/null
@@ -1,7923 +0,0 @@
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diff --git a/tutorials/med3pa_experiment_results/test/models_evaluation.json b/tutorials/med3pa_experiment_results/test/models_evaluation.json
deleted file mode 100644
index 3a210d1..0000000
--- a/tutorials/med3pa_experiment_results/test/models_evaluation.json
+++ /dev/null
@@ -1,10 +0,0 @@
-{
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\ No newline at end of file
diff --git a/tutorials/med3pa_experiment_results/test/profiles.json b/tutorials/med3pa_experiment_results/test/profiles.json
deleted file mode 100644
index 5b17c80..0000000
--- a/tutorials/med3pa_experiment_results/test/profiles.json
+++ /dev/null
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index 920583e..a2ff62b 100644
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