Day 18 of 133

Cross-validation & data leakage + DSA Binary Search

k-fold, stratified, time-series CV, nested CV, leak prevention.

DSA · NeetCode Binary Search

Binary SearchDSA · Binary Search
Interview questions to prep
1. What's your binary-search template (left ≤ right, left < right) and which one are you using and why?
2. Walk through the bug-prone bits: mid calculation overflow, off-by-one on bounds.
Search A 2d MatrixDSA · Binary Search
Interview questions to prep
1. State your loop invariant precisely — what must be true on every iteration?
2. Why does the loop terminate, and how do you avoid infinite loops on the search-space update?
3. Walk through edge cases: empty array, target smaller than min, target larger than max, duplicates.
Koko Eating BananasDSA · Binary Search
Interview questions to prep
1. State your loop invariant precisely — what must be true on every iteration?
2. Why does the loop terminate, and how do you avoid infinite loops on the search-space update?
3. Walk through edge cases: empty array, target smaller than min, target larger than max, duplicates.

k-fold, stratified, leave-one-out, time-series CVTraditional MLscikit-learn
Interview questions to prep
1. When does k-fold leak data, and what does TimeSeriesSplit do differently?
2. Why is stratified k-fold important for imbalanced classification?
Train/val/test split & nested CVTraditional MLStatQuest
Interview questions to prep
1. Why do you need both a validation and a test set for hyperparameter tuning?
2. What is nested cross-validation and when is it worth the cost?
3. How would your split strategy change for time-series forecasting vs random tabular rows?
Common forms of data leakageTraditional MLMLM
Interview questions to prep
1. Walk through three common ways data leakage sneaks into an ML pipeline.
2. How would you build a pipeline that prevents leakage when scaling features?

References & further reading