Explainability
Global Surrogates
Approximates entire model behaviour with simpler model (e.g., GAMs, Model Distillation)
4 techniques in this subcategory
4 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| Ridge Regression Surrogates | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | This technique approximates a complex model by training a ridge regression (a linear model with L2 regularisation) on... | ||
| RuleFit | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Any | RuleFit creates interpretable surrogate models that can explain complex black-box models or serve as interpretable... | ||
| Model Distillation | Algorithmic | Architecture/neural Networks Paradigm/parametric +3 | Any | Model distillation transfers knowledge from a large, complex model (teacher) to a smaller, more efficient model... | ||
| Generalized Additive Models | Algorithmic | Architecture/linear Models/gam Paradigm/parametric +2 | Tabular | An intrinsically interpretable modelling technique that extends linear models by allowing flexible, nonlinear... |
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