From b5f63490eca1864a6d86b0259be9a6d8bd2d2771 Mon Sep 17 00:00:00 2001 From: ArturoAmorQ Date: Wed, 3 May 2023 11:09:13 +0200 Subject: [PATCH 1/4] DOC Add HGBDT to see also section of random forests --- sklearn/ensemble/_forest.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/sklearn/ensemble/_forest.py b/sklearn/ensemble/_forest.py index de729264e35c0..0743e4747587d 100644 --- a/sklearn/ensemble/_forest.py +++ b/sklearn/ensemble/_forest.py @@ -1336,6 +1336,8 @@ class labels (multi-output problem). sklearn.tree.DecisionTreeClassifier : A decision tree classifier. sklearn.ensemble.ExtraTreesClassifier : Ensemble of extremely randomized tree classifiers. + sklearn.ensemble.HistGradientBoostingClassifier : Gradient boosting that + is a non-parametric model accepting monotonicity constraints. Notes ----- @@ -1672,6 +1674,8 @@ class RandomForestRegressor(ForestRegressor): sklearn.tree.DecisionTreeRegressor : A decision tree regressor. sklearn.ensemble.ExtraTreesRegressor : Ensemble of extremely randomized tree regressors. + sklearn.ensemble.HistGradientBoostingRegressor : Gradient boosting that + is a non-parametric model accepting monotonicity constraints. Notes ----- From 2cdc2ff9b7f19da5d5d86d22f6cf1bff6ca58542 Mon Sep 17 00:00:00 2001 From: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com> Date: Thu, 4 May 2023 10:13:58 +0200 Subject: [PATCH 2/4] Update sklearn/ensemble/_forest.py Co-authored-by: Tim Head --- sklearn/ensemble/_forest.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/sklearn/ensemble/_forest.py b/sklearn/ensemble/_forest.py index 0743e4747587d..46b5ebcaabe5e 100644 --- a/sklearn/ensemble/_forest.py +++ b/sklearn/ensemble/_forest.py @@ -1674,8 +1674,7 @@ class RandomForestRegressor(ForestRegressor): sklearn.tree.DecisionTreeRegressor : A decision tree regressor. sklearn.ensemble.ExtraTreesRegressor : Ensemble of extremely randomized tree regressors. - sklearn.ensemble.HistGradientBoostingRegressor : Gradient boosting that - is a non-parametric model accepting monotonicity constraints. + sklearn.ensemble.HistGradientBoostingRegressor : A Histogram-based Gradient Boosting Regression Tree, very fast for big datasets (n_samples >= 10 000). Notes ----- From 0cfaee26d09545f90ca4cf267e2496697c8b6df5 Mon Sep 17 00:00:00 2001 From: ArturoAmorQ Date: Thu, 4 May 2023 10:16:39 +0200 Subject: [PATCH 3/4] Fix format --- sklearn/ensemble/_forest.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/sklearn/ensemble/_forest.py b/sklearn/ensemble/_forest.py index 46b5ebcaabe5e..64d1e68e0525c 100644 --- a/sklearn/ensemble/_forest.py +++ b/sklearn/ensemble/_forest.py @@ -1674,7 +1674,9 @@ class RandomForestRegressor(ForestRegressor): sklearn.tree.DecisionTreeRegressor : A decision tree regressor. sklearn.ensemble.ExtraTreesRegressor : Ensemble of extremely randomized tree regressors. - sklearn.ensemble.HistGradientBoostingRegressor : A Histogram-based Gradient Boosting Regression Tree, very fast for big datasets (n_samples >= 10 000). + sklearn.ensemble.HistGradientBoostingRegressor : A Histogram-based Gradient + Boosting Regression Tree, very fast for big datasets (n_samples >= + 10_000). Notes ----- From 3791082b90e9bf978c7216c336cbebd6c1a35ec3 Mon Sep 17 00:00:00 2001 From: ArturoAmorQ Date: Thu, 4 May 2023 10:19:19 +0200 Subject: [PATCH 4/4] Update RandomForestClassifier for consistency --- sklearn/ensemble/_forest.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/sklearn/ensemble/_forest.py b/sklearn/ensemble/_forest.py index 64d1e68e0525c..c01fdf7fe7069 100644 --- a/sklearn/ensemble/_forest.py +++ b/sklearn/ensemble/_forest.py @@ -1336,8 +1336,9 @@ class labels (multi-output problem). sklearn.tree.DecisionTreeClassifier : A decision tree classifier. sklearn.ensemble.ExtraTreesClassifier : Ensemble of extremely randomized tree classifiers. - sklearn.ensemble.HistGradientBoostingClassifier : Gradient boosting that - is a non-parametric model accepting monotonicity constraints. + sklearn.ensemble.HistGradientBoostingRegressor : A Histogram-based Gradient + Boosting Classification Tree, very fast for big datasets (n_samples >= + 10_000). Notes -----