Machine Learning Interview Questions and Answers

Data leakage is when information unavailable at prediction time seeps into training, producing optimistic validation scores that collapse in production.

Common sources:

• Target leakage: a feature that is a proxy for the label, such as account_closed_date when predicting churn.
• Train-test contamination: fitting scalers, imputers, target encoders or SMOTE on the full dataset before splitting.
• Temporal leakage: using future data to predict the past, as with random splitting of time series.
• Group leakage: the same entity appears in both train and test.

Prevention:

• Split first, then fit all preprocessing on training folds only, ideally inside a pipeline.
• Use time-based splits for temporal problems.
• Audit features for whether they are known at prediction time.
• Use grouped splits when rows share an entity.

Pipeline([("scaler", StandardScaler()), ("clf", LogisticRegression())])

Leakage often shows as suspiciously high accuracy, so treat such results with suspicion.

ROC-AUC is the probability that a randomly chosen positive example is ranked above a randomly chosen negative one. It summarizes performance across all thresholds, and 0.5 is random.

from sklearn.metrics import roc_auc_score
roc_auc_score(y_true, y_scores)

Misleading cases:

• Heavy class imbalance: ROC-AUC can look strong while precision is terrible, because the large negative class dominates the false-positive rate. Precision-recall AUC is more informative when the positive class is rare.
• Threshold independence: AUC says nothing about the operating point you will deploy.
• Calibration: a model can rank well but produce badly calibrated probabilities, which matters when decisions depend on the probability value.
• Ranking versus cost: if errors have asymmetric costs, AUC ignores them.

Report AUC alongside PR-AUC, calibration curves and the confusion matrix at the chosen threshold.

Start with the objective and data: implicit signals such as views, clicks and purchases, plus explicit ratings and item or user features.

A common two-stage design:

• Candidate generation: retrieve a few hundred relevant items quickly. Use collaborative filtering such as matrix factorization or ALS, content-based similarity, or approximate nearest neighbour search over embeddings. This scales to millions of items.
• Ranking: score candidates with a richer model, gradient boosting or a neural network, using user, item and context features to optimize the target event.

user_vec = als_model.user_factors[user_id]
scores = item_factors @ user_vec
candidates = np.argpartition(scores, -200)[-200:]

Add business rules for diversity, freshness and filtering. Evaluate offline with recall@k and NDCG, then run online A/B tests on engagement and revenue. Address the cold start problem with popularity or content features, and monitor feedback loops and drift.