Handling Class Imbalance Questions

Addressing scenarios where one class significantly outnumbers others (common in fraud, churn, disease detection). Problems: accuracy becomes misleading (95% accuracy can be trivial if 95% are negative class), model biased toward majority class. Solutions: Resampling (undersampling majority, oversampling minority, or SMOTE), adjusting class weights in loss function, choosing appropriate metrics (F1, precision-recall instead of accuracy), ensemble methods. For junior level, recognize imbalance problems, understand why accuracy fails, and know multiple approaches to handling it.

HardTechnical

0 practiced

Data engineering / SQL: write an efficient SQL query pattern that returns a balanced batch for training from a large events table (columns: event_id, event_time, user_id, label) where positives are rare. The query should return all positives in a recent time window and a random sample of negatives matched by day. Also describe a streaming approach to continuously generate balanced mini-batches without full-table scans.

MediumTechnical

0 practiced

Coding task (medium): implement a simplified SMOTE function in Python for numeric features. Signature: def simple_smote(X_minority, n_samples, k=5, random_state=None): -> returns numpy array of synthetic samples. Use Euclidean distance and nearest neighbors; discuss time and memory complexity and how to scale to large minority sets.

HardTechnical

0 practiced

Discuss risks of naive undersampling of the majority class and propose 'smart' undersampling methods such as ClusterCentroids, NearMiss, Tomek links, and informed undersampling based on influence functions. Explain how to validate that an undersampling strategy did not remove important majority-class modes.

HardTechnical

0 practiced

Case study: you inherit a training pipeline that applies SMOTE before splitting data and results show train AUC 0.98 but validation AUC 0.75 and declining minority recall in production. Walk through a debugging plan: list specific checks (data leakage, order of operations, SMOTE paramization, neighbor analysis), experiments to compare alternatives, and a remediation plan with short-term and long-term fixes.

MediumTechnical

0 practiced

Label noise scenario: minority-class labels are noisy due to human annotation errors. Provide a prioritized list of strategies to mitigate label noise given a limited labeling budget: how would you estimate noise rate, decide which examples to relabel, and which robust loss/functions or training strategies to apply?

Unlock Full Question Bank

Get access to hundreds of Handling Class Imbalance interview questions and detailed answers.

Join thousands of developers preparing for their dream job.