Model Interpretation¶

Last Updated: 3 months ago2024-12-26 ; Contributors: AhmedThahir, web-flow

Association $\ne$ Causation

Classification of Inference Techniques¶

	IDK	Scope
Simple Linear Regression $y = \beta_0 + \beta_j x_j + \beta_\text{ind} G + \beta_\text{int} x_j G$	Model-Specific	Global	$\beta_0$ is the baseline value of $y$ when $x_j=0$ $\beta_j$ is the change in $y$ for every unit increase in $x_j$ $\beta_\text{ind}$ is the change in baseline for group $G$ , ie baseline will now be $(\beta_0 + \beta_\text{ind})$ $\beta_\text{int}$ is the additional change in $y$ in group $G$ , ie for every unit increase in $x_j$ , $y$ changes by $(\beta_j + \beta_\text{int})$ units for group $G$
$\ln \vert y \vert = \beta_0 + \beta_j x_j$	Model-Specific	Global	For every unit increase in $x_j$ , percentage change in $y$ is $\beta_j$ units
$\ln \vert y \vert = \beta_0 + \beta_j \ln \vert x_j \vert$	Model-Specific	Global	Elasticity of $y$ wrt $x_j$ is given by $\beta_j$ $\beta_j = \dfrac{\% \Delta y}{\% \Delta x_j}$
SAGE	Model-Agnostic	Global
Variable/Feature Importance	Model-Agnostic	Global	Decrease of in-sample error due to splits over $x$, averaged over all trees of ensemble
Partial Dependence	Model-Agnostic	Global	Partial derivative of $y$ wrt $x$: Marginal effect of $x$ on $y$ after integrating out all other vars
SHAP	Model-Agnostic	Local
LIME	Model-Agnostic	Local