Random Forest trains many decision trees on random subsets of your data and combines their votes into a final prediction. It handles mixed feature types well, is resistant to overfitting, and requires minimal tuning.Documentation Index
Fetch the complete documentation index at: https://docs.clickml.app/llms.txt
Use this file to discover all available pages before exploring further.
Configuration
| Parameter | Description | Default |
|---|---|---|
| N Estimators | Number of trees in the forest. More trees = more stable but slower. | 100 |
| Max Depth | Maximum depth of each tree. Leave empty for unlimited. | None |
| Min Samples Split | Minimum samples required to split a node. | 2 |
| Min Samples Leaf | Minimum samples required in a leaf node. | 1 |
| Random State | Seed for reproducibility. | 42 |
Input / Output
| Type | |
|---|---|
| Input | X Train + Y Train (from Train-Test Split) |
| Output | Trained Model |