梯度提升树回归：class sklearn.ensemble.GradientBoostingRegressor(loss=’ls’, learning_rate=0.1, n_estimators=100, subsample=1.0, criterion=’friedman_mse’, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3, min_impurity_decrease=0.0, min_impurity_split=None, init=None, random_state=None, max_features=None, alpha=0.9, verbose=0, max_leaf_nodes=None, warm_start=False, presort=’auto’)
随机森林回归：class sklearn.ensemble.RandomForestRegressor(n_estimators=10, criterion=’mse’, max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=’auto’, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False)
高斯过程回归：class sklearn.gaussian_process.GaussianProcessRegressor(kernel=None, alpha=1e-10, optimizer=’fmin_l_bfgs_b’, n_restarts_optimizer=0, normalize_y=False, copy_X_train=True, random_state=None)
保序回归：class sklearn.isotonic.IsotonicRegression(y_min=None, y_max=None, increasing=True, out_of_bounds=’nan’)
Lasso回归：class sklearn.linear_model.Lasso(alpha=1.0, fit_intercept=True, normalize=False, precompute=False, copy_X=True, max_iter=1000, tol=0.0001, warm_start=False, positive=False, random_state=None, selection=’cyclic’)
线性回归：class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)
岭回归： class sklearn.linear_model.Ridge(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver=’auto’, random_state=None)
KNN回归：class sklearn.neighbors.KNeighborsRegressor(n_neighbors=5, weights=’uniform’, algorithm=’auto’, leaf_size=30, p=2, metric=’minkowski’, metric_params=None, n_jobs=1, **kwargs)
多层感知神经网络回归：class sklearn.neural_network.MLPRegressor(hidden_layer_sizes=(100, ), activation=’relu’, solver=’adam’, alpha=0.0001, batch_size=’auto’, learning_rate=’constant’, learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
SVM回归：class sklearn.svm.SVR(kernel=’rbf’, degree=3, gamma=’auto’, coef0=0.0, tol=0.001, C=1.0, epsilon=0.1, shrinking=True, cache_size=200, verbose=False, max_iter=-1)
决策树回归：class sklearn.tree.DecisionTreeRegressor(criterion=’mse’, splitter=’best’, max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, presort=False)
评价：可解释方差值：sklearn.metrics.explained_variance_score(y_true, y_pred, sample_weight=None, multioutput=’uniform_average’)
平均绝对误差：sklearn.metrics.mean_absolute_error(y_true, y_pred, sample_weight=None, multioutput=’uniform_average’)[source]
均方误差：sklearn.metrics.mean_squared_error(y_true, y_pred, sample_weight=None, multioutput=’uniform_average’)
均方对数误差：sklearn.metrics.mean_squared_log_error(y_true, y_pred, sample_weight=None, multioutput=’uniform_average’)
中值绝对误差：sklearn.metrics.median_absolute_error(y_true, y_pred)
R²值：sklearn.metrics.r2_score(y_true, y_pred, sample_weight=None, multioutput=’uniform_average’)
八、demo from sklearn import neighbors, datasets, preprocessing from sklearn.cross_validation import train_test_split from sklearn.metrics import accuracy_score iris = datasets.load_iris() X, y = iris.data, iris.target X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33) scaler = preprocessing.StandardScaler().fit(X_train) X_train = scaler.transform(X_train) X_test = scaler.transform(X_test) knn = neighbors.KNeighborsClassifier(n_neighbors=5) knn.fit(X_train, y_train) y_pred = knn.predict(X_test) accuracy_score(y_test, y_pred) 四、绘图 1、创建画布或子图 函数名称 函数作用plt.figure 创建一个空白画布，可以指定画布大小，像素。
figure.add_subplot 创建并选中子图，可以指定子图的行数，列数，与选中图片编号。
2、绘制 函数名称 函数作用
plt.title 在当前图形中添加标题，可以指定标题的名称，位置，颜色，字体大小等参数。
plt.xlabel 在当前图形中添加x轴名称，可以指定位置，颜色，字体大小等参数。
plt.ylabel 在当前图形中添加y轴名称，可以指定位置，颜色，字体大小等参数。
plt.xlim 指定当前图形x轴的范围，只能确定一个数值区间，而无法使用字符串标识。
plt.ylim 指定当前图形y轴的范围，只能确定一个数值区间，而无法使用字符串标识。
plt.xticks 指定x轴刻度的数目与取值
plt.yticks 指定y轴刻度的数目与取值
plt.legend 指定当前图形的图例，可以指定图例的大小，位置，标签。
3、中文 plt.rcParams['font.sans-serif'] = 'SimHei' ##设置字体为SimHei显示中文 plt.rcParams['axes.unicode_minus'] = False ##设置正常显示符号 4、不同图形