[ENH] Add option to permute per forest fraction

doc/whats_new/v0.3.rst

@@ -16,6 +16,7 @@ Changelog
 - |Fix| Fixes a bug where covariate indices were not shuffled by default when running FeatureImportanceForestClassifier and FeatureImportanceForestRegressor test methods, by `Sambit Panda`_ (:pr:`140`)
 - |Enhancement| Add multi-view splitter for axis-aligned decision trees, by `Adam Li`_ (:pr:`129`)
 - |Enhancement| Add stratified sampling option to ``FeatureImportance*`` via the ``stratify`` keyword argument, by `Yuxin Bai`_ (:pr:`143`)
+- |API| ``FeatureImportanceForest*`` now has a hyperparameter to control the number of permutations is done per forest ``permute_per_forest_fraction``, by `Adam Li`_ (:pr:`145`)
 
 Code and Documentation Contributors
 -----------------------------------

examples/hypothesis_testing/plot_MI_genuine_hypothesis_testing_forest.py

@@ -120,7 +120,7 @@
 stat, pvalue = est.test(
     X, y, covariate_index=np.arange(n_features_set, dtype=int), metric="mi", n_repeats=n_repeats
 )
-print(f"Estimated MI difference: {stat} with Pvalue: {pvalue}")
+print(f"Estimated MI difference for the important feature set: {stat} with Pvalue: {pvalue}")
 
 # we test for the second feature set, which is unimportant and thus should return a pvalue > 0.05
 stat, pvalue = est.test(
@@ -130,7 +130,7 @@
     metric="mi",
     n_repeats=n_repeats,
 )
-print(f"Estimated MI difference: {stat} with Pvalue: {pvalue}")
+print(f"Estimated MI difference for the unimportant feature set: {stat} with Pvalue: {pvalue}")
 
 # %%
 # References