From f83f2a50c8aca0c58e11561065ad5d0b13bf31ee Mon Sep 17 00:00:00 2001 From: Stephan Steinfurt Date: Sat, 12 Oct 2019 11:58:04 +0200 Subject: [PATCH 1/3] Add example for ensemble.BaggingRegressor --- sklearn/ensemble/_bagging.py | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/sklearn/ensemble/_bagging.py b/sklearn/ensemble/_bagging.py index d40f1717f469f..e8a3a4e407212 100644 --- a/sklearn/ensemble/_bagging.py +++ b/sklearn/ensemble/_bagging.py @@ -935,6 +935,22 @@ class BaggingRegressor(RegressorMixin, BaseBagging): `oob_prediction_` might contain NaN. This attribute exists only when ``oob_score`` is True. + Examples + -------- + >>> from sklearn.svm import SVR + >>> from sklearn.ensemble import BaggingRegressor + >>> from sklearn.datasets import make_regression + >>> X, y = make_regression(n_samples=100, n_features=4, + ... n_informative=2, n_targets=1, + ... random_state=0, shuffle=False) + >>> regr = BaggingRegressor(n_estimators=10, random_state=0).fit(X, y) + >>> regr.predict([[0, 0, 0, 0]]) + array([2.36503199]) + >>> regr = BaggingRegressor(base_estimator=SVR(), + ... n_estimators=10, random_state=0).fit(X, y) + >>> regr.predict([[0, 0, 0, 0]]) + array([-2.87202411]) + References ---------- From 01c11b993b383725b14675ea340a65f1c6f33787 Mon Sep 17 00:00:00 2001 From: Stephan Steinfurt Date: Sat, 12 Oct 2019 12:02:56 +0200 Subject: [PATCH 2/3] Increase speed of ensemble.BaggingClassifier --- sklearn/ensemble/_bagging.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/sklearn/ensemble/_bagging.py b/sklearn/ensemble/_bagging.py index e8a3a4e407212..4d95b18a4518e 100644 --- a/sklearn/ensemble/_bagging.py +++ b/sklearn/ensemble/_bagging.py @@ -546,14 +546,14 @@ class BaggingClassifier(ClassifierMixin, BaseBagging): >>> from sklearn.svm import SVC >>> from sklearn.ensemble import BaggingClassifier >>> from sklearn.datasets import make_classification - >>> X, y = make_classification(n_samples=1000, n_features=4, + >>> X, y = make_classification(n_samples=100, n_features=4, ... n_informative=2, n_redundant=0, ... random_state=0, shuffle=False) - >>> clf = BaggingClassifier(n_estimators=100, random_state=0).fit(X, y) + >>> clf = BaggingClassifier(n_estimators=10, random_state=0).fit(X, y) >>> clf.predict([[0, 0, 0, 0]]) array([1]) >>> clf = BaggingClassifier(base_estimator=SVC(), - ... n_estimators=100, random_state=0).fit(X, y) + ... n_estimators=10, random_state=0).fit(X, y) >>> clf.predict([[0, 0, 0, 0]]) array([1]) From 157b730caea70659c41bab93a9dc7b27e0a5220b Mon Sep 17 00:00:00 2001 From: Stephan Steinfurt Date: Fri, 18 Oct 2019 08:45:25 +0200 Subject: [PATCH 3/3] Implement just one example and relax accuracy --- sklearn/ensemble/_bagging.py | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/sklearn/ensemble/_bagging.py b/sklearn/ensemble/_bagging.py index 4d95b18a4518e..5c62f8e2411a3 100644 --- a/sklearn/ensemble/_bagging.py +++ b/sklearn/ensemble/_bagging.py @@ -549,9 +549,6 @@ class BaggingClassifier(ClassifierMixin, BaseBagging): >>> X, y = make_classification(n_samples=100, n_features=4, ... n_informative=2, n_redundant=0, ... random_state=0, shuffle=False) - >>> clf = BaggingClassifier(n_estimators=10, random_state=0).fit(X, y) - >>> clf.predict([[0, 0, 0, 0]]) - array([1]) >>> clf = BaggingClassifier(base_estimator=SVC(), ... n_estimators=10, random_state=0).fit(X, y) >>> clf.predict([[0, 0, 0, 0]]) @@ -943,13 +940,10 @@ class BaggingRegressor(RegressorMixin, BaseBagging): >>> X, y = make_regression(n_samples=100, n_features=4, ... n_informative=2, n_targets=1, ... random_state=0, shuffle=False) - >>> regr = BaggingRegressor(n_estimators=10, random_state=0).fit(X, y) - >>> regr.predict([[0, 0, 0, 0]]) - array([2.36503199]) >>> regr = BaggingRegressor(base_estimator=SVR(), ... n_estimators=10, random_state=0).fit(X, y) >>> regr.predict([[0, 0, 0, 0]]) - array([-2.87202411]) + array([-2.8720...]) References ----------