# -*- coding: utf-8 -*- """ Created on Wed May 9 09:53:30 2018 @author: admin """ import numpy as np import matplotlib.pyplot as plt from sklearn import ensemble from sklearn import datasets from sklearn.utils import shuffle from sklearn.metrics import mean_squared_error # ############################################################################# # Load data boston = datasets.load_boston() X, y = shuffle(boston.data, boston.target, random_state=13) X = X.astype(np.float32) offset = int(X.shape[0] * 0.9) X_train, y_train = X[:offset], y[:offset] X_test, y_test = X[offset:], y[offset:] # ############################################################################# # Fit regression model params = {'n_estimators': 500, 'max_depth': 4, 'min_samples_split': 2, 'learning_rate': 0.01, 'loss': 'ls'} #随便指定参数长度，也不用在传参的时候去特意定义一个数组传参 clf = ensemble.GradientBoostingRegressor(**params) clf.fit(X_train, y_train) mse = mean_squared_error(y_test, clf.predict(X_test)) print("MSE: %.4f" % mse) # ############################################################################# # Plot training deviance # compute test set deviance test_score = np.zeros((params['n_estimators'],), dtype=np.float64) for i, y_pred in enumerate(clf.staged_predict(X_test)): test_score[i] = clf.loss_(y_test, y_pred) plt.figure(figsize=(12, 6)) plt.subplot(1, 2, 1) plt.title('Deviance') plt.plot(np.arange(params['n_estimators']) + 1, clf.train_score_, 'b-', label='Training Set Deviance') plt.plot(np.arange(params['n_estimators']) + 1, test_score, 'r-', label='Test Set Deviance') plt.legend(loc='upper right') plt.xlabel('Boosting Iterations') plt.ylabel('Deviance') # ############################################################################# # Plot feature importance feature_importance = clf.feature_importances_ # make importances relative to max importance feature_importance = 100.0 * (feature_importance / feature_importance.max()) sorted_idx = np.argsort(feature_importance) pos = np.arange(sorted_idx.shape[0]) + .5 plt.subplot(1, 2, 2) plt.barh(pos, feature_importance[sorted_idx], align='center') plt.yticks(pos, boston.feature_names[sorted_idx]) plt.xlabel('Relative Importance') plt.title('Variable Importance') plt.show()
房产数据介绍：
- CRIM per capita crime rate by town
- ZN proportion of residential land zoned for lots over 25,000 sq.ft.
- INDUS proportion of non-retail business acres per town
- CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
- NOX nitric oxides concentration (parts per 10 million)
- RM average number of rooms per dwelling
- AGE proportion of owner-occupied units built prior to 1940
- DIS weighted distances to five Boston employment centres
- RAD index of accessibility to radial highways
- TAX full-value property-tax rate per $10,000
- PTRATIO pupil-teacher ratio by town
- B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
- LSTAT % lower status of the population
- MEDV Median value of owner-occupied homes in $1000's
方法：
