MIchael Mayer builds some effect plots:
The plots show different types of feature effects relevant in modeling:
- Average observed: Descriptive effect (also interesting without model).
- Average predicted: Combined effect of all features. Also called “M Plot” (Apley 2020).
- Partial dependence: Effect of one feature, keeping other feature values constant (Friedman 2001).
- Number of observations or sum of case weights: Feature value distribution.
- R only: Accumulated local effects, an alternative to partial dependence (Apley 2020).
Click through to see how they both work.