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 - What are the main assumptions of linear regression?
Linearity between features and target, independent errors, constant error variance (homoscedasticity), roughly normal errors (needed mainly for confidence intervals), and no severe multicollinearity among features.
IntermediateQuick answer1 min read