Gradient boosting (XGBoost, LightGBM, CatBoost) + DSA 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?

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?

Interview questions to prep

1. Walk me through how GBM fits each new tree on the negative gradient of the loss.
2. Why is GBM more sensitive to learning rate than Random Forest?

Interview questions to prep

1. What does XGBoost do differently from vanilla GBM that made it dominate Kaggle?
2. How does XGBoost handle missing values automatically?

Interview questions to prep

1. When would you pick LightGBM over XGBoost?
2. What problem does CatBoost's ordered boosting solve?