From 78de256f594a5a88c188ecf18d9b9095209ba222 Mon Sep 17 00:00:00 2001 From: Soledad Galli Date: Sun, 6 Apr 2025 13:09:08 -0300 Subject: [PATCH 1/3] fix typo in docstring --- boruta/boruta_py.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/boruta/boruta_py.py b/boruta/boruta_py.py index a248f7b..0c3bb8c 100644 --- a/boruta/boruta_py.py +++ b/boruta/boruta_py.py @@ -49,7 +49,7 @@ class BorutaPy(BaseEstimator, TransformerMixin): - Ranking of features: After fitting BorutaPy it provides the user with ranking of features. Confirmed ones are 1, Tentatives are 2, and the rejected are ranked - starting from 3, based on their feautre importance history through + starting from 3, based on their feature importance history through the iterations. We highly recommend using pruned trees with a depth between 3-7. From 052cde141f5217160fcea2e5da50c37bb5c7e4d0 Mon Sep 17 00:00:00 2001 From: Soledad Galli Date: Sun, 6 Apr 2025 13:28:25 -0300 Subject: [PATCH 2/3] fix another typo --- boruta/boruta_py.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/boruta/boruta_py.py b/boruta/boruta_py.py index 0c3bb8c..a32919b 100644 --- a/boruta/boruta_py.py +++ b/boruta/boruta_py.py @@ -138,7 +138,7 @@ class BorutaPy(BaseEstimator, TransformerMixin): support_weak_ : array of shape [n_features] The mask of selected tentative features, which haven't gained enough - support during the max_iter number of iterations.. + support during the max_iter number of iterations. ranking_ : array of shape [n_features] From fd89f2a3ca2f3603dedeb299673b78fe3ba5281d Mon Sep 17 00:00:00 2001 From: Soledad Galli Date: Sun, 6 Apr 2025 22:11:43 -0300 Subject: [PATCH 3/3] fix another typo --- boruta/boruta_py.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/boruta/boruta_py.py b/boruta/boruta_py.py index a32919b..31702f7 100644 --- a/boruta/boruta_py.py +++ b/boruta/boruta_py.py @@ -43,7 +43,7 @@ class BorutaPy(BaseEstimator, TransformerMixin): crucial parameter. For more info, please read about the perc parameter. - Automatic tree number: Setting the n_estimator to 'auto' will calculate the number of trees - in each itartion based on the number of features under investigation. + in each iteration based on the number of features under investigation. This way more trees are used when the training data has many features and less when most of the features have been rejected. - Ranking of features: