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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- 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