Skip to content

Latest commit

 

History

History
92 lines (62 loc) · 2.9 KB

README.md

File metadata and controls

92 lines (62 loc) · 2.9 KB
Raw

Naive Bayes

Tests

Naive Bayes works by looking at a training set and making a guess based on that set. It uses simple statistics and a bit of math to calculate the result.

What can I use this for?

You can use this for categorizing any text content into any arbitrary set of categories. For example:

  • is an email spam, or not spam ?
  • is a news article about technology, politics, or sports ?
  • is a piece of text expressing positive emotions, or negative emotions?

Installation

You may install Naive Bayes into your project using the Composer package manager:

composer require assisted-mindfulness/naive-bayes

Learning

Before the algorithm can do anything, it requires a training set with historical information. To teach your classifier which category the text belongs to, call the learn method:

$classifier = new Classifier();

$classifier
    ->learn('I love sunny days', 'positive')
    ->learn('I hate rain', 'negative');

Guessing

After you have trained the classifier, you can use the prediction of which category the transmitted text belongs to, for example:

$classifier->most('is a sunny days'); // positive
$classifier->most('there will be rain'); // negative

In order for you to enter more similar information, you can use:

$classifier->guess('is a sunny days');

/*
items: array:2 [
  "positive" => 0.0064
  "negative" => 0.0039062
]
*/

Uneven

When the training set contains unbalanced data not intentionally but due to insufficient data, you can enable an 'uneven' mode that equalizes the probability calculation for document types.

$classifier
   ->uneven()
   ->guess('is a sunny days');

Tokenizer

The algorithm utilizes a tokenizer to segment the text into words. By default, it splits the text by spaces and includes words with a length of more than 3 symbols. You can also define your custom tokenizer using the following example:

$classifier = new Classifier();

$classifier->setTokenizer(function (string $string) {
    return Str::of($string)
        ->lower()
        ->matchAll('/[[:alpha:]]+/u')
        ->filter(fn (string $word) => Str::length($word) > 3);
});

Wrapping up

There you have it! Even with a very small training set the algorithm can still return some decent results. For example, Naive Bayes has been proven to give decent results in sentiment analyses.

Moreover, Naive Bayes can be applied to more than just text. If you have other ways of calculating the probabilities of your metrics you can also plug those in and it will just as good.

License

The MIT License (MIT). Please see License File for more information.