applicable models
tree based
Techniques for decision trees, random forests, and gradient boosting
3 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| Mean Decrease Impurity | Algorithmic | Tree Based | Tabular | Mean Decrease Impurity (MDI) quantifies a feature's importance in tree-based models (e.g., Random Forests, Gradient... | ||
| Monotonicity Constraints | Algorithmic | Tree Based Gaussian Process | Tabular | Monotonicity constraints enforce consistent directional relationships between input features and model predictions,... | ||
| Intrinsically Interpretable Models | Algorithmic | Tree Based Linear | Any | Intrinsically interpretable models are machine learning algorithms that are transparent by design, allowing users to... |
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