🟪 1-Minute Summary
Gradient Boosting builds models sequentially, each trying to correct errors (residuals) of the previous ensemble. Uses gradient descent to minimize loss function. More flexible than AdaBoost (works for regression too). Hyperparameters: learning rate (shrinkage), n_estimators, max_depth. Pros: state-of-the-art performance. Cons: prone to overfitting, slow training, many hyperparameters to tune.
🟦 Core Notes (Must-Know)
How Gradient Boosting Works
[Content to be filled in]
The Algorithm
[Content to be filled in]
- Initialize with simple model
- Calculate residuals (errors)
- Train new model on residuals
- Add to ensemble with learning rate
- Repeat
Key Hyperparameters
[Content to be filled in]
- n_estimators
- learning_rate
- max_depth
- subsample
When to Use Gradient Boosting
[Content to be filled in]
🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
-
“How does Gradient Boosting work?”
- [Answer: Sequential models fitting residuals, gradient descent optimization]
-
“What’s the learning rate in Gradient Boosting?”
- [Answer: Shrinkage factor controlling each tree’s contribution]
-
“Gradient Boosting vs AdaBoost?”
- [Answer: GB more flexible (regression), uses gradient descent, generally better]
🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Using high learning rate
[Content to be filled in - overfitting]
Mistake 2: Too many estimators
[Content to be filled in]
🟩 Mini Example (Quick Application)
Scenario
[Regression with Gradient Boosting]
Solution
from sklearn.ensemble import GradientBoostingRegressor
# Example to be filled in
🔗 Related Topics
Navigation: