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 do you interpret a coefficient in logistic regression?
Each coefficient is the change in log-odds for a one-unit rise in that feature. Exponentiate it to get an odds ratio: a coefficient of 0.693 multiplies the odds by about 2.
IntermediateQuick answer1 min read - What is log loss (cross-entropy), and why does logistic regression not use mean squared error?
Log loss is -ln(p) for the probability given to the true class. It punishes confident wrong predictions very heavily. With a sigmoid, squared error gives a non-convex loss and weak gradients; log loss is convex and gives clean gradients.
IntermediateQuick answer1 min read - When would you pick an SVM over logistic regression, and the other way round?
Pick logistic regression when you want calibrated probabilities, speed and interpretable coefficients. Pick an SVM (often with RBF kernel) when classes are not linearly separable and the dataset is small to medium. Both need scaled features.
IntermediateQuick answer1 min read