梯度提升树分类：class sklearn.ensemble.GradientBoostingClassifier(loss=’deviance’, 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, verbose=0, max_leaf_nodes=None, warm_start=False, presort=’auto’)
随机森林分类：class sklearn.ensemble.RandomForestClassifier(n_estimators=10, criterion=’gini’, 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, class_weight=None)
高斯过程分类：class sklearn.gaussian_process.GaussianProcessClassifier(kernel=None, optimizer=’fmin_l_bfgs_b’, n_restarts_optimizer=0, max_iter_predict=100, warm_start=False, copy_X_train=True, random_state=None, multi_class=’one_vs_rest’, n_jobs=1)
逻辑回归：class sklearn.linear_model.LogisticRegression(penalty=’l2’, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver=’liblinear’, max_iter=100, multi_class=’ovr’, verbose=0, warm_start=False, n_jobs=1)
KNN：class sklearn.neighbors.KNeighborsClassifier(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：class sklearn.neural_network.MLPClassifier(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：class sklearn.svm.SVC(C=1.0, kernel=’rbf’, degree=3, gamma=’auto’, coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape=’ovr’, random_state=None)
决策树：class sklearn.tree.DecisionTreeClassifier(criterion=’gini’, 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, class_weight=None, presort=False)
评价：准确率：sklearn.metrics.accuracy_score(y_true, y_pred, normalize=True, sample_weight=None)
AUC：sklearn.metrics.auc(x, y, reorder=False)
分类报告：sklearn.metrics.classification_report(y_true, y_pred, labels=None, target_names=None, sample_weight=None, digits=2)
混淆矩阵：sklearn.metrics.confusion_matrix(y_true, y_pred, labels=None, sample_weight=None)
kappa：sklearn.metrics.cohen_kappa_score(y1, y2, labels=None, weights=None, sample_weight=None)
F1值：sklearn.metrics.f1_score(y_true, y_pred, labels=None, pos_label=1, average=’binary’, sample_weight=None)
精确率：sklearn.metrics.precision_score(y_true, y_pred, labels=None, pos_label=1, average=’binary’, sample_weight=None)
召回率：sklearn.metrics.recall_score(y_true, y_pred, labels=None, pos_label=1, average=’binary’, sample_weight=None)
ROC：sklearn.metrics.roc_curve(y_true, y_score, pos_label=None, sample_weight=None, drop_intermediate=True)
7、回归 常用算法：Adaboost回归：class sklearn.ensemble.AdaBoostRegressor(base_estimator=None, n_estimators=50, learning_rate=1.0, loss=’linear’, random_state=None)