ALGORITHM OF NAIVE BAYES METHODS IN BINARY CLASSIFICATION TASKS ON SANTANDER DATASET EXAMPLE FROM KAGGLE PLATFORM

Abstract

The actual output of many binary classification algorithms is a prediction score. The score indicates the system's certainty that the given observation belongs to the positive class. Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in many real-world applications, despite the strong assumption that all features are conditionally independent given the class. In the learning process of this classifier with the known structure, class probabilities and conditional probabilities are calculated using training data, and then values of these probabilities are used to classify new observations. To make the decision about whether the observation should be classified as positive or negative, you will interpret the score by picking a classification threshold (cut-off) and compare the score against it. Any observations with scores higher than the threshold are then predicted as the positive class and scores lower than the threshold are predicted as the negative class. However, AUC is independent of the selected threshold, you can get a sense of the prediction performance of your model from the AUC metric without picking a threshold