classical ml

SVMs sort the candidates who memorized 'maximize the margin' from those who can explain how a kernel delivers non-linear separation without ever touching the high-dimensional space. Here is the answer that lands the second signal.

Two simple classifiers that keep showing up in interviews because they check whether you grasp assumptions and tradeoffs, not just sklearn calls. What matters is k-NN's laziness and curse of dimensionality against Naive Bayes' independence assumption. Here is the answer.

Most candidates can recite 'maximize the margin'. The dual is where you prove you really understand why only support vectors matter and where the kernel trick originates. Here is the derivation an interviewer wants.

Both label sequences, but one is generative and one is discriminative, and that gap decides whether you can add overlapping features. Here is the comparison that proves you understand the modeling tradeoff, not merely the acronyms.

Anyone can say 'ensemble of trees'. The real answer concerns decorrelating those trees and why that, not averaging alone, is what drops variance. Here is what makes the forest more than the sum of its trees.

XGBoost made boosting practical; LightGBM made it fast. The answer is histogram binning, gradient-based sampling, and leaf-wise growth, each carrying a real tradeoff. Here is what they do and where they hurt.

The 'naive' independence assumption is wrong on real data, yet the classifier remains a strong baseline for text. The interesting answer explains why classification survives a broken assumption, plus smoothing and the variants. Here it is.

k-NN looks trivial until you ask about picking k, why distances lose meaning in high dimensions, and how to keep prediction fast on millions of points. Here is the tradeoff-aware answer.

Univariate ranking is the trap everyone falls into: it retains redundant features and discards ones that only matter in combination. Here is the filter/wrapper/embedded breakdown plus mRMR and Boruta, and where each one falls short.

Lasso delivers sparsity, Ridge copes with correlated features, and each stumbles where the other excels. Elastic Net combines them for a specific, common failure. Here is when it earns its two hyperparameters.

Resampling, reweighting, and focal loss tackle class imbalance from different angles, and each carries a real downside. The signal is matching the method to the model and metric, not blindly oversampling. Here is the breakdown.