.. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_ensemble_plot_gradient_boosting_early_stopping.py: =================================== 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. .. code-block:: default # Authors: Vighnesh Birodkar # Raghav RV # 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 ---------------------------------------------- .. code-block:: default 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') 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() .. image:: /auto_examples/ensemble/images/sphx_glr_plot_gradient_boosting_early_stopping_001.png :alt: plot gradient boosting early stopping :class: sphx-glr-single-img Compare fit times with and without early stopping ------------------------------------------------- .. code-block:: default 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() .. image:: /auto_examples/ensemble/images/sphx_glr_plot_gradient_boosting_early_stopping_002.png :alt: plot gradient boosting early stopping :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 51.906 seconds) .. _sphx_glr_download_auto_examples_ensemble_plot_gradient_boosting_early_stopping.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.23.X?urlpath=lab/tree/notebooks/auto_examples/ensemble/plot_gradient_boosting_early_stopping.ipynb :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_gradient_boosting_early_stopping.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_gradient_boosting_early_stopping.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_