Naive Bayes
The "naive" assumption is wrong, and it works anyway. That's the beauty of it. Language: Python Implement Multinomial Naive Bayes from scratch with Laplace smoothing for text classification. Explain why the naive independence assumption is mathematically wrong but produces correct class rankings in practice. Compare Multinomial, Bernoulli, and Gaussian Naive Bayes variants and select the right one for a given feature type. Evaluate Naive Bayes against logistic regression on high-dimensional sparse data and explain the bias-variance tradeoff at work. You need to classify text. Emails into spam or not-spam. Customer reviews into positive or negative. Support tickets into categories. You have thousands of features (one per word) and limited training data. Most classifiers choke here. Logistic regression needs enough samples to estimate thousands of weights reliably. Decision trees split on one word at a time and overfit wildly. KNN in 10,000 dimensions is meaningless because every point is equally far from every other point. Naive Bayes handles this. It makes a mathematically wrong assumption (that every feature is independent of every other feature given the class), and it still outperforms "smarter" models on text classification, especially with small training sets. It trains in a single pass through the data. It scales to millions of features. It produces probability estimates (though often poorly calibrated due to the independence assumption). Understanding why…
Naive Bayes: The "naive" assumption is wrong, and it works anyway. That's the beauty of it. Language: Python Implement Multinomial Naive Bayes from scratch…
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