Machine Learning Algorithms and Theory Questions

Core supervised and unsupervised machine learning algorithms and the theoretical principles that guide their selection and use. Covers linear regression, logistic regression, decision trees, random forests, gradient boosting, support vector machines, k means clustering, hierarchical clustering, principal component analysis, and anomaly detection. Topics include model selection, bias variance trade off, regularization, overfitting and underfitting, ensemble methods and why they reduce variance, computational complexity and scaling considerations, interpretability versus predictive power, common hyperparameters and tuning strategies, and practical guidance on when each algorithm is appropriate given data size, feature types, noise, and explainability requirements.

MediumTechnical

23 practiced

Explain how you would detect multicollinearity in a dataset and practical strategies to address it for predictive modeling and for inference. Cover variance inflation factor (VIF), condition number, principal components or regularization, and feature grouping or dropping based on domain knowledge.

EasyTechnical

31 practiced

Explain how decision trees choose splits for classification and regression tasks. Compare Gini impurity and entropy for classification and mean-squared-error for regression, and describe how continuous and categorical features are handled. Explain common strategies to prevent trees from overfitting.

EasyTechnical

37 practiced

Define overfitting and underfitting. Provide a prioritized, practical checklist of steps (data, model, training, validation, deployment) you would take to reduce overfitting in a production ML model, and briefly explain why each step helps.

Sample Answer

Overfitting: model learns noise or spurious patterns from training data, performing well on train but poorly on unseen data. Underfitting: model is too simple to capture underlying patterns, performing poorly on both train and test.

Prioritized practical checklist to reduce overfitting (ordered by typical impact):

Data1. Increase/collect more data — more examples reduce variance and let the model generalize. 2. Data augmentation (images, text paraphrasing, noise injection) — expands effective dataset and teaches invariances. 3. Clean label noise / remove outliers — prevents learning incorrect signals.

Model4. Simplify architecture (fewer layers/parameters) — reduces capacity to memorize noise. 5. Use appropriate model family (e.g., tree vs. neural net) — match complexity to problem.

Training6. Regularization (L2/L1) — penalizes large weights, discouraging complex fits. 7. Dropout / batch normalization — improves robustness and acts as noise regularizer. 8. Early stopping monitored on validation loss — halts before memorization begins. 9. Use proper optimization hyperparameters (smaller learning rate, weight decay) — stable convergence reduces overfitting risk.

Validation10. Strong validation strategy: holdout, k-fold CV, or time-based splits — provides realistic generalization estimate. 11. Monitor multiple metrics and learning curves — detect divergence between train/test early. 12. Use a separate test set only for final evaluation — avoid leaking validation into training.

Deployment / Production13. Model ensembling (bagging, averaging) — reduces variance by combining diverse predictors. 14. Continuous monitoring (data drift, performance) — detect when model begins to overfit new distributions. 15. Retrain pipeline and A/B test before rollouts — ensure updates improve real-world performance.

Why these help: they either reduce model capacity relative to data, increase effective training signal, penalize complexity, or provide realistic checks so you catch overfitting early and respond in production.

MediumTechnical

22 practiced

For a time-series forecasting problem, explain how to adapt cross-validation to respect temporal order. Describe walk-forward (rolling) validation, expanding vs sliding window strategies, how to tune hyperparameters without leaking future information, and how to handle seasonality and non-stationarity in fold selection.

Sample Answer

For time-series forecasting you must preserve temporal order to avoid leakage. The standard approach is walk‑forward (rolling) validation: repeatedly train on past data and test on the immediate future, advancing the cut‑point forward each fold.

- Walk‑forward validation (process): - Choose initial train window, choose test horizon (e.g., 1 day / 1 week / 1 season). - For each fold t: train on data up to time T_t, validate on (T_t, T_t + h]. - Advance T_t and repeat to collect out‑of‑time errors.

- Expanding vs sliding window: - Expanding window: train set grows each fold (train = [0..T_t]). Good when you want to use all historical info and model can handle data drift; reduces variance of estimates. - Sliding (rolling) window: fixed-size window that moves forward (train = [T_t−w+1..T_t]). Good when older data becomes irrelevant due to non‑stationarity or regime shifts.

- Hyperparameter tuning without leakage: - Nest validation: for each outer walk‑forward fold, perform inner hyperparameter search using only data up to T_t (use inner rolling splits within the training window). Choose hyperparameters based on inner validation, then evaluate once on the outer test fold. - Never use any future test fold data during tuning. Use time‑aware CV objects (e.g., sklearn’s TimeSeriesSplit or custom splitter).

- Handling seasonality and non‑stationarity in fold selection: - Ensure each validation fold contains full seasonal cycles (e.g., include at least one season) so error reflects seasonal performance. - If strong non‑stationarity, prefer recent data (sliding window) or weight recent observations more (time‑decay) and include more frequent folds near recent periods. - For periodic events (holidays), stratify folds so both train and test include those events when relevant. - Monitor performance drift across folds; if performance degrades over time, consider model retraining cadence or regime‑based splits.

Best practices: report aggregated metrics across walk‑forward folds, inspect fold‑wise residuals for drift, and use nested time‑aware tuning to avoid optimistic bias.

EasyTechnical

29 practiced

Explain the bias–variance trade-off in supervised learning. Describe how training error and validation error typically change as model complexity increases, give concrete examples (e.g., linear model vs deep neural net), and list practical techniques to move towards lower expected generalization error.

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