Note
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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 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 <[email protected]> # Raghav RV <[email protected]> # 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]
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()
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()
Total running time of the script: ( 0 minutes 20.319 seconds)
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