Interview Q&A / Machine Learning
Machine Learning interview questions
Supervised vs unsupervised learning, overfitting, metrics and the classic models, explained simply.
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- Why is Naive Bayes called 'naive', and why does it still work?
It naively assumes that features are independent of each other once the class is known. That is rarely true, but classification only needs the correct class to have the highest score, so predictions are often good even when the probabilities are off.
BeginnerQuick answer1 min read - How does Naive Bayes classify an email as spam? Work through a small example.
It uses Bayes' theorem: posterior is proportional to prior times the product of each word's likelihood given the class. Compute that score for spam and for not-spam, then normalise. The class with the higher posterior wins.
IntermediateQuick answer1 min read - What is Laplace smoothing in Naive Bayes, and why is it needed?
If a word never appeared with a class in training, its probability is zero and wipes out the whole product. Laplace smoothing adds 1 to every word count so no probability is exactly zero.
IntermediateQuick answer1 min read - What is the difference between Gaussian, Multinomial and Bernoulli Naive Bayes?
Gaussian NB assumes each feature is normally distributed within a class, so it suits continuous values. Multinomial NB models word counts. Bernoulli NB models binary present/absent features.
IntermediateQuick answer1 min read