3_3_2.ipynb

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            "text/plain": [
              "       spam  not_spam\n",
              "mails    23        25\n",
              "words   161       193"
            ],
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              "      <th></th>\n",
              "      <th>spam</th>\n",
              "      <th>not_spam</th>\n",
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              "  <tbody>\n",
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              "      <th>mails</th>\n",
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      ],
      "source": [
        "import pandas as pd\n",
        "import io\n",
        "import numpy as np\n",
        "spam_data = pd.DataFrame(np.array([\n",
        "    [23, 25],\n",
        "    [161, 193]\n",
        "]),index =['mails','words'], columns=['spam', 'not_spam'])\n",
        "\n",
        "spam_data"
      ]
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        "train_data = pd.DataFrame(np.array([\n",
        "    [10, 36],\n",
        "    [8, 0],\n",
        "    [1, 16],\n",
        "    [13, 9],\n",
        "    [31, 2],\n",
        "    [10, 5],\n",
        "    [9, 6],\n",
        "    [0, 16],\n",
        "    [34, 16],\n",
        "    [45, 87]\n",
        "]),index =['Investment','Refund','Online','Million','Coupon','Prize','Access','Bill','Bonus','Free'],\n",
        "                          columns=['spam', 'not_spam'])\n",
        "\n",
        "train_data\n"
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              "            spam  not_spam\n",
              "Investment    10        36\n",
              "Refund         8         0\n",
              "Online         1        16\n",
              "Million       13         9\n",
              "Coupon        31         2\n",
              "Prize         10         5\n",
              "Access         9         6\n",
              "Bill           0        16\n",
              "Bonus         34        16\n",
              "Free          45        87"
            ],
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              "      <td>13</td>\n",
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              "      <th>Free</th>\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "\n",
        "# Создание модели наивного байесовского классификатора\n",
        "model = MultinomialNB(alpha=1)  # alpha=1 для сглаживания по Лапласу\n"
      ],
      "metadata": {
        "id": "j5xGhV0MxRtQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = train_data.values.T  # Переводим данные в формат, подходящий для sklearn\n",
        "y_train = np.array(['spam', 'not_spam'])  # Метки классов\n",
        "\n",
        "model.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "mrbGmLub1XGF",
        "outputId": "dd71ccbb-a0f8-4bc9-fb69-241df465cab9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB(alpha=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB(alpha=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB(alpha=1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "words_to_predict = np.array([\n",
        "    [0, 1, 1, 0, 0, 1, 1, 0, 0, 0]  # 'Free', 'Offer', 'Access', 'Purchase', 'Prize', 'Bonus', 'Online'\n",
        "])\n",
        "\n",
        "# Предсказание класса для новых данных\n",
        "predicted_class = model.predict(words_to_predict)\n"
      ],
      "metadata": {
        "id": "QBRd_PNu1gt_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Предсказание вероятности классов для новых данных\n",
        "class_probabilities = model.predict_proba(words_to_predict)\n",
        "\n",
        "# Вероятность того, что предсказанный класс является \"spam\"\n",
        "spam_probability = class_probabilities[0][0]\n"
      ],
      "metadata": {
        "id": "L3UucYyO1iEJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rounded_spam_probability = round(spam_probability, 3)\n",
        "print(rounded_spam_probability)\n"
      ],
      "metadata": {
        "id": "PmKQT9vC1lI1",
        "outputId": "e8a408fc-d5e8-459f-8132-07544864f487",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.154\n"
          ]
        }
      ]
    }
  ]
}