docs

Author: mathias-von-ottenbreit

documentation/model_interpretation.md

@@ -0,0 +1,10 @@
+# The recommended way to interpret an APLR model
+
+## Main effects
+Use the ***get_main_effect_shape*** method to interpret main effects as shown in this [example](https://github.com/ottenbreit-data-science/aplr/blob/main/examples/train_aplr_regression.py). For each main effect, plot the output in a scatter plot.
+
+## Interactions
+For best interpretability of interactions, do not use a higher ***max_interaction_level*** than 1. Use the ***calculate_local_contribution_from_selected_terms*** method to interpret interactions as shown in this [example](https://github.com/ottenbreit-data-science/aplr/blob/main/examples/train_aplr_regression.py). For each interaction of interest you can plot the output in a 3D scatter plot.
+
+## Interpretation of model terms and their regression coefficients
+For an example on how to interpret the terms in an APLR model, please see ***Section 5.1.3*** in the published article about APLR. You can find this article on [https://link.springer.com/article/10.1007/s00180-024-01475-4](https://link.springer.com/article/10.1007/s00180-024-01475-4) and [https://rdcu.be/dz7bF](https://rdcu.be/dz7bF).

documentation/model_interpretation_example.md

@@ -113,13 +113,13 @@
 )
 
 # Local (observation specific) contribution to the linear predictor from selected interacting predictors.
-# In this example this concerns two-way interaction terms in the model where the second and the third predictors in X interact.
+# In this example this concerns two-way interaction terms in the model where the fourth and the seventh predictors in X interact.
 # The local contribution will be zero for all observations if there are no such terms in the model.
 # The local contribution can help interpreting interactions (or main effects if only one predictor index is specified).
-# In this example, the local contribution can be plotted against the predictor values for a visual interpretation.
+# For two-way interactions the local contribution can be plotted against the predictor values in a 3D scatter plot.
 contribution_from_selected_terms = (
     best_model.calculate_local_contribution_from_selected_terms(
-        X=data_train[predictors], predictor_indexes=[1, 2]
+        X=data_train[predictors], predictor_indexes=[3, 6]
     )
 )