Ruby is a versatile programming language known for its simplicity and ease of use. While there are sophisticated libraries available for text classification, understanding the inner workings of a Bayesian text classifier can be enlightening. In this article, we’ll embark on a journey to create a Bayesian text classifier in Ruby from scratch, helping you grasp the fundamentals of text classification.

Understanding Bayesian Text Classification

Bayesian text classification is a probabilistic approach to categorizing text documents into predefined categories, such as spam or not spam. It relies on Bayes’ theorem to calculate the probability of a document belonging to a particular category based on the words it contains and prior knowledge.

Creating a Bayesian Classifier in Ruby

We’ll start by building a basic Bayesian text classifier in Ruby without using external libraries. The code below provides an overview of the implementation:

class BayesianTextClassifier
  def initialize(categories)
    @categories = categories
    @category_word_counts = { |h, k| h[k] = }
    @category_document_counts =
    @total_documents = 0

  def train(category, document)
    @total_documents += 1
    @category_document_counts[category] += 1

    words = document.split
    words.each do |word|
      @category_word_counts[category][word] += 1

  def classify(document)
    best_category = nil
    max_probability = -1.0 / 0.0

    @categories.each do |category|
      probability = calculate_probability(category, document)
      if probability > max_probability
        max_probability = probability
        best_category = category



  def calculate_probability(category, document)
    category_probability = @category_document_counts[category].to_f / @total_documents

    words = document.split
    word_probabilities = do |word|
      word_probability(category, word)

    Math.log(category_probability) + word_probabilities.sum

  def word_probability(category, word)
    word_count = @category_word_counts[category][word]
    category_document_count = @category_document_counts[category]
    total_word_count = @category_word_counts.sum { |_, counts| counts[word] }

    (word_count + 1) / (total_word_count + category_document_count)

# Example usage
classifier =['Spam', 'Not Spam'])

classifier.train('Spam', 'Buy cheap luxury watches')
classifier.train('Not Spam', 'Hi, how are you doing today?')
classifier.train('Spam', 'Congratulations! You\'ve won a million dollars!')
classifier.train('Not Spam', 'Could you please review this document?')

message_to_classify = 'Claim your prize now!'
classification_result = classifier.classify(message_to_classify)

puts "The message '#{message_to_classify}' is classified as '#{classification_result}'"


Creating a Bayesian text classifier from scratch in Ruby is a rewarding experience that deepens your understanding of text classification algorithms. While this basic example serves as a foundation, real-world applications often involve more complex techniques and external libraries. However, building your own classifier allows you to appreciate the inner workings and principles of Bayesian text classification. Whether you’re exploring machine learning or enhancing your Ruby skills, this project provides valuable insights into the world of text analysis and classification.