AI and machine learning interview questions

Heteroscedasticity means residual variance changes with x or with the predicted value.

It minimizes log loss, which is the negative log likelihood of a Bernoulli model.

One-vs-rest fits a binary model per class.

5 only matches equal costs and a well-calibrated model with balanced classes.

It struggles with strongly nonlinear decision surfaces unless you add interactions or basis functions.

Pruning removes branches that add little validated gain so the tree does not memorize noise.

A feature with many levels can create many pure-looking splits by chance.

ID3 uses information gain and likes categorical splits.

Yes, order-preserving transforms such as log or min-max usually leave the split sequence unchanged for a single tree.

Each tree is trained on a bootstrap sample, so some rows are left out of that tree.

A smaller feature subset at each split makes trees more diverse and usually lowers correlation among them.

Impurity importance favors high-cardinality and correlated features.

Extremely randomized trees pick split thresholds more randomly, not just the feature subset.

Gradient boosting fits each new tree to the gradient of the loss with respect to the current ensemble prediction.

The learning rate shrinks each new tree's contribution so later trees correct more gently.

AdaBoost reweights training rows so misclassified points get more attention in the next weak learner.

Boosting keeps chasing remaining train error, so extra rounds can fit noise.

C trades off margin width against training mistakes.

Both are linear classifiers.

RBF SVMs scale badly with n because they depend on many support vectors.