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

0 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.

MediumTechnical

1 practiced

How would you calibrate the probability outputs of a classifier and why does calibration matter for business decisions? Compare Platt scaling and isotonic regression, explain how to validate calibration (reliability diagram, Brier score), and describe cross-validation or holdout strategies to avoid overfitting the calibrator.

EasyTechnical

5 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.

EasyTechnical

1 practiced

Explain principal component analysis (PCA): its objective, the steps to compute principal components using covariance eigendecomposition or SVD, assumptions and when PCA is appropriate. Describe necessary preprocessing (centering, scaling), and practical methods to choose the number of components (explained variance, scree plot).

EasyTechnical

3 practiced

Explain the area under the ROC curve (AUC-ROC): what it measures about a classifier, how to interpret its value, and situations where AUC can be misleading (e.g., severe class imbalance or when calibration matters). Suggest alternative metrics in those cases and justify them.

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