ML
ML Interview Roadmap
← Study plan·Traditional ML

Day 23 of 133

Imbalanced classification + DSA Linked List

SMOTE / class weights / focal loss. PR-AUC, F-beta, MCC.

DSA · NeetCode Linked List

  • Copy List With Random PointerDSA · Linked List

    Interview questions to prep

    1. Walk through your pointer hazards — what breaks if you lose track of the head or a prev pointer?
    2. Can you do this in-place (O(1) extra space)? What's the trick?
    3. How would you detect / handle a cycle, and prove your method's correctness?
  • Add Two NumbersDSA · Linked List

    Interview questions to prep

    1. Walk through your pointer hazards — what breaks if you lose track of the head or a prev pointer?
    2. Can you do this in-place (O(1) extra space)? What's the trick?
    3. How would you detect / handle a cycle, and prove your method's correctness?

ML · Imbalanced classification

  • Resampling, class weights, SMOTETraditional MLimbalanced-learn

    Interview questions to prep

    1. Compare random oversampling, undersampling, SMOTE, and class weighting — when does each help?
    2. Why can SMOTE leak when applied before cross-validation?
  • Focal loss & cost-sensitive learningTraditional MLLin et al.

    Interview questions to prep

    1. Why does focal loss help with extreme imbalance in object detection?
    2. Compare focal loss vs class-weighted cross-entropy — when does focal actually win?
  • Metric choice for imbalance: PR-AUC, F-beta, MCCTraditional MLMLM

    Interview questions to prep

    1. Why is accuracy a terrible metric for imbalanced classification?
    2. When do you reach for F1, F-beta, MCC, or PR-AUC?

References & further reading