scikit-learn · ogrisel · Aug 9, 2017 · Nov 2, 2015 · Jul 20, 2016 · Jul 20, 2016
diff --git a/doc/whats_new.rst b/doc/whats_new.rst
@@ -5,6 +5,26 @@
 Release history
 ===============
 
+Version 0.20 (under development)
+================================
+
+Changed models
+--------------
+
+Changelog
+---------
+
+New features
+............
+
+Classifiers and regressors
+
+- :class:`ensemble.GradientBoostingClassifier` and
+  :class:`ensemble.GradientBoostingRegressor` now support early stopping
+  via ``n_iter_no_change``, ``validation_fraction`` and ``tol``. :issue:`7071`
+  by `Raghav RV`_
+
+
 Version 0.19
 ============
 

diff --git a/examples/ensemble/plot_gradient_boosting_early_stopping.py b/examples/ensemble/plot_gradient_boosting_early_stopping.py
@@ -0,0 +1,160 @@
+"""
+===================================
+Early stopping of Gradient Boosting
+===================================
+
+Gradient boosting is an ensembling technique where several weak learners
+(regression trees) are combined to yield a powerful single model, in an
+iterative fashion.
+
+Early stopping support in Gradient Boosting enables us to find the least number
+of iterations which is sufficient to build a model that generalizes well to
+unseen data.
+
+The concept of early stopping is simple. We specify a ``validation_fraction``
+which denotes the fraction of the whole dataset that will be kept aside from
+training to assess the validation loss of the model. The gradient boosting
+model is trained using the training set and evaluated using the validation set.
+When each additional stage of regression tree is added, the validation set is
+used to score the model.  This is continued until the scores of the model in
+the last ``n_iter_no_change`` stages do not improve by atleast `tol`. After
+that the model is considered to have converged and further addition of stages
+is "stopped early".
+
+The number of stages of the final model is available at the attribute
+``n_estimators_``.
+
+This example illustrates how the early stopping can used in the
+:class:`sklearn.ensemble.GradientBoostingClassifier` model to achieve
+almost the same accuracy as compared to a model built without early stopping
+using many fewer estimators. This can significantly reduce training time,
+memory usage and prediction latency.
+"""
+
+# Authors: Vighnesh Birodkar <vighneshbirodkar@nyu.edu>
+#          Raghav RV <rvraghav93@gmail.com>
+# License: BSD 3 clause
+
+import time
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from sklearn import ensemble
+from sklearn import datasets
+from sklearn.model_selection import train_test_split
+
+print(__doc__)
+
+data_list = [datasets.load_iris(), datasets.load_digits()]
+data_list = [(d.data, d.target) for d in data_list]
+data_list += [datasets.make_hastie_10_2()]
+names = ['Iris Data', 'Digits Data', 'Hastie Data']
+
+n_gb = []
+score_gb = []
+time_gb = []
+n_gbes = []
+score_gbes = []
+time_gbes = []
+
+n_estimators = 500
+
+for X, y in data_list:
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
+                                                        random_state=0)
+
+    # We specify that if the scores don't improve by atleast 0.01 for the last
+    # 10 stages, stop fitting additional stages
+    gbes = ensemble.GradientBoostingClassifier(n_estimators=n_estimators,
+                                               validation_fraction=0.2,
+                                               n_iter_no_change=5, tol=0.01,
+                                               random_state=0)
+    gb = ensemble.GradientBoostingClassifier(n_estimators=n_estimators,
+                                             random_state=0)
+    start = time.time()
+    gb.fit(X_train, y_train)
+    time_gb.append(time.time() - start)
+
+    start = time.time()
+    gbes.fit(X_train, y_train)
+    time_gbes.append(time.time() - start)
+
+    score_gb.append(gb.score(X_test, y_test))
+    score_gbes.append(gbes.score(X_test, y_test))
+
+    n_gb.append(gb.n_estimators_)
+    n_gbes.append(gbes.n_estimators_)
+
+bar_width = 0.2
+n = len(data_list)
+index = np.arange(0, n * bar_width, bar_width) * 2.5
+index = index[0:n]
+
+#######################################################################
+# Compare scores with and without early stopping
+# ----------------------------------------------
+
+plt.figure(figsize=(9, 5))
+
+bar1 = plt.bar(index, score_gb, bar_width, label='Without early stopping',
+               color='crimson')
+bar2 = plt.bar(index + bar_width, score_gbes, bar_width,
+               label='With early stopping', color='coral')
+
+max_y = np.amax(np.maximum(score_gb, score_gbes))
+
+plt.xticks(index + bar_width, names)
+plt.yticks(np.arange(0, 1.3, 0.1))
+
+
+def autolabel(rects, n_estimators):
+    """
+    Attach a text label above each bar displaying n_estimators of each model
+    """
+    for i, rect in enumerate(rects):
+        plt.text(rect.get_x() + rect.get_width() / 2.,
+                 1.05 * rect.get_height(), 'n_est=%d' % n_estimators[i],
+                 ha='center', va='bottom')
+
+
+autolabel(bar1, n_gb)
+autolabel(bar2, n_gbes)
+
+plt.ylim([0, 1.3])
+plt.legend(loc='best')
+plt.grid(True)
+
+plt.xlabel('Datasets')
+plt.ylabel('Test score')
+
+plt.show()
+
+
+#######################################################################
+# Compare fit times with and without early stopping
+# ----------------------------------------------
+
+plt.figure(figsize=(9, 5))
+
+bar1 = plt.bar(index, time_gb, bar_width, label='Without early stopping',
+               color='crimson')
+bar2 = plt.bar(index + bar_width, time_gbes, bar_width,
+               label='With early stopping', color='coral')
+
+max_y = np.amax(np.maximum(time_gb, time_gbes))
+
+plt.xticks(index + bar_width, names)
+plt.yticks(np.linspace(0, 1.3 * max_y, 13))
+
+autolabel(bar1, n_gb)
+autolabel(bar2, n_gbes)
+
+plt.ylim([0, 1.3 * max_y])
+plt.legend(loc='best')
+plt.grid(True)
+
+plt.xlabel('Datasets')
+plt.ylabel('Fit Time')
+
+plt.show()