This documentation explains the CNaiveBayes class in MQL5, which implements a Naive Bayes classifier for classification tasks.

I. Naive Bayes Theory:

Naive Bayes is a probabilistic classifier based on Bayes' theorem. It assumes that the features used for classification are independent of each other given the class label. This simplifies the calculations involved in making predictions.

II. CNaiveBayes Class:

The CNaiveBayes class provides functionalities for training and using a Naive Bayes classifier in MQL5:

Public Functions:

• CNaiveBayes(void): Constructor.
• ~CNaiveBayes(void): Destructor.
• void fit(matrix &x, vector &y): Trains the model on the provided data (x - features, y - target labels).
• int predict(vector &x): Predicts the class label for a single input vector.
• vector predict(matrix &x): Predicts class labels for all rows in the input matrix.

Internal Variables:

• n_features: Number of features in the data.
• y_target: Vector of target labels used during training.
• classes: Vector containing the available class labels.
• class_proba: Vector storing the prior probability of each class.
• features_proba: Matrix storing the conditional probability of each feature value given each class.
• c_prior_proba: Vector storing the calculated prior probability of each class after training.
• c_evidence: Vector storing the calculated class evidence for a new data point.
• calcProba(vector &v_features): Internal function (not directly accessible) that likely calculates the class probabilities for a given feature vector.

III. Class Functionality:

1. Training:

   • The fit function takes the input data (features and labels) and performs the following:
     • Calculates the prior probability of each class (number of samples belonging to each class divided by the total number of samples).
     • Estimates the conditional probability of each feature value given each class (using techniques like Laplace smoothing to handle unseen features).
   • These probabilities are stored in the internal variables for later use in prediction.

2. Prediction:

   • The predict functions take a new data point (feature vector) and:
     • Calculate the class evidence for each class using Bayes' theorem, considering the prior probabilities and conditional probabilities of the features.
     • The class with the highest class evidence is predicted as the most likely class for the new data point.

IV. Additional Notes:

• The class assumes the data is already preprocessed and ready for use.

Reference

• Data Science and Machine Learning (Part 11): Naïve Bayes, Probability theory in Trading