简单的GBDT调用见官方例子：
方法地址：http://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regression.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-regression-py
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 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:] params = { ' n_estimators ' : 500 , ' max_depth ' : 4 , ' min_samples_split ' : 2 , ' learning_rate ' : 0.01 , ' loss ' : ' ls ' } clf = ensemble.GradientBoostingRegressor( ** params) clf.fit(X_train, y_train) # 训练 mse = mean_squared_error(y_test, clf.predict(X_test)) # 预测并且计算MSE # print 2.7和3.0版本有区别 print " MSE: %.4f " % mse # compute test set deviance test_score = np.zeros((params[ ' n_estimators ' ],), dtype = np.float64) # 矩阵转化 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()
输出：
结论：LSTAT>RM>DIS………
房产的数据结构：
- 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
源代码读取csv文件方法详解：
def load_boston(): """Load and return the boston house-prices dataset (regression). ============== ============== Samples total 506 Dimensionality 13 Features real, positive Targets real 5. - 50. ============== ============== Returns ------- data : Bunch Dictionary-like object, the interesting attributes are: 'data', the data to learn, 'target', the regression targets, and 'DESCR', the full description of the dataset. Examples -------- >>> from sklearn.datasets import load_boston >>> boston = load_boston() >>> print(boston.data.shape) (506, 13) """ module_path = dirname(__file__) fdescr_name = join(module_path, 'descr', 'boston_house_prices.rst') with open(fdescr_name) as f: #with用法可以自动关闭，读取文件声明 descr_text = f.read() data_file_name = join(module_path, 'data', 'boston_house_prices.csv') with open(data_file_name) as f: data_file = csv.reader(f) #csv读取文件，获得迭代器 temp = next(data_file) #读取文件头，获得总行数和字段名 n_samples = int(temp[0]) n_features = int(temp[1]) data = np.empty((n_samples, n_features)) #建立矩阵 target = np.empty((n_samples,)) temp = next(data_file) # names of features feature_names = np.array(temp) for i, d in enumerate(data_file): #一行一行通过迭代器读取文件设置到矩阵中 data[i] = np.asarray(d[:-1], dtype=np.float) target[i] = np.asarray(d[-1], dtype=np.float) return Bunch(data=data, target=target, # last column is target value feature_names=feature_names[:-1], DESCR=descr_text)
方法说明：
1、with是python的一种用法，可以自动关闭对应的资源
2、next(iterator[, default]) 迭代器遍历方法
3、reader(csvfile, dialect='excel', **fmtparams) ：返回一个迭代器
Return a reader object which will iterate over lines in the given csvfile.
类的主要方法介绍：
构造函数： loss learning_rate n_estimators max_depth criterion min_samples_split min_samples_leaf min_weight_fraction_leaf subsample max_features max_leaf_nodes min_impurity_split alpha init verbose warm_start random_state presort
属性
方法 feature_importances_ ：重要的特性 oob_improvement_： train_score_ ：训练分数 loss_ ：损失函数 `init` : 评估期 estimators_ : ndarray of DecisionTreeRegressor, shape = [n_estimators, 1]
方法：
后续待补充详细参数说明
