Supervised Naive Bayes Classification Algorithm with Non-independent Features

This article explores a fascinating machine learning algorithm—the supervised naive Bayes classification algorithm. Unlike conventional classifiers, this approach incorporates dependencies between features, significantly enhancing classification accuracy and reliability. The implementation typically involves calculating conditional probabilities using maximum likelihood estimation or Bayesian estimation methods. We will examine the parameter estimation techniques and demonstrate how to process input training and test datasets to generate classification results with accuracy metrics. Key implementation steps include: feature dependency modeling through covariance matrices or correlation analysis, probability calculation using smoothed frequency counts, and prediction via argmax classification rules. Through this technical exploration, you will gain deeper insights into this innovative machine learning algorithm and master its practical application techniques for real-world classification tasks.