Gradient Boosting builds trees one at a time, with each tree correcting the mistakes of the previous ensemble. It often achieves higher accuracy than Random Forest but requires more careful tuning.Documentation Index
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Configuration
| Parameter | Description | Default |
|---|---|---|
| N Estimators | Number of boosting stages (trees). | 100 |
| Learning Rate | Shrinks the contribution of each tree. Lower = more conservative, needs more trees. | 0.1 |
| Max Depth | Maximum depth of individual trees. | 3 |
| Subsample | Fraction of training samples used per tree. Values below 1.0 add randomness (stochastic gradient boosting). | 1.0 |
| Random State | Seed for reproducibility. | 42 |
Input / Output
| Type | |
|---|---|
| Input | X Train + Y Train |
| Output | Trained Model |