1. How do you handle class imbalance in classification?
Resampling (SMOTE/undersample), class weights, threshold tuning, PR-AUC over accuracy.
Machine Learning interview questions and answers for data scientists and ML engineers
Machine learning interview questions for data roles range from bias-variance and evaluation through to model and system design. Real ML questions asked for data-science and ML-engineering loops, with worked answers below.
10 Machine Learning questions
2. Explain L1 vs L2 regularization.
L1 (Lasso) drives weights to zero for sparsity and selection, L2 (Ridge) shrinks weights smoothly.
3. What metrics would you use for an imbalanced fraud model?
Precision, recall, PR-AUC and F-beta. Avoid raw accuracy, and consider cost-weighted metrics.
4. Explain how a random forest works.
Bagged decision trees with feature subsampling, averaged or voted to reduce variance.
5. What is cross-validation and why use k-fold?
Split data into k folds to estimate generalization robustly and use all data for validation.
6. How would you detect data leakage in a pipeline?
Check for features that use future or target information, fit transforms only on the training set, and audit unrealistically high scores.
7. Explain precision vs recall with an example.
Precision is the share of predicted positives that are correct, recall is the share of actual positives that were caught.
8. What is gradient boosting and how does it differ from bagging?
Boosting fits trees sequentially on residuals to reduce bias, bagging trains them independently to reduce variance.
9. How do you choose the number of clusters in k-means?
Elbow method, silhouette score, gap statistic, or domain knowledge.
10. Explain how you'd build an uplift/causal model for a marketing campaign.
Model treatment effect (T-learner/uplift trees) using randomized/quasi-experimental data.