Implement Naive Bayes classifier from scratch
Company: Pinterest
Role: Machine Learning Engineer
Category: Machine Learning
Difficulty: hard
Interview Round: Online Assessment
Implement a **Naive Bayes** classifier from scratch (you may use NumPy).
Write a class with:
- `fit(X, y)`: estimate class priors and feature likelihood parameters.
- `predict(X)`: compute posteriors (or unnormalized log-posteriors) and output the most likely class.
Specify which variant you implement (e.g., **Multinomial Naive Bayes** for count features, **Bernoulli NB** for binary features, or **Gaussian NB** for continuous features). Your implementation should clearly compute:
- prior \(P(y=c)\)
- likelihood parameters for \(P(x\mid y=c)\)
- posterior scoring using Bayes’ rule with numerical stability (typically log-probabilities).
Overview: This question evaluates understanding of probabilistic classification, specifically competence in estimating class priors, feature likelihood parameters, and computing posterior scores for Naive Bayes variants.
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