max_features=6, random_state=1) clf.fit(boston.data, boston.target)score= clf.score(boston.data, boston.target)assertscore<3, ("Failed with max_features=None, ""criterion %s andscore= %f"% (c,score))# Extra-treesclf = ExtraTreesRegressor(n_estimators=5, criterion=c, random_state=1)...
regr = MLPRegressor( random_state=42 ,hidden_layer_sizes=(4,2) ,max_iter=50000 #,activation='relu' ) regr = regr.fit(X_train,y_train) # 输出预测结果 y_pred = regr.predict(X_test) #y_pred # 输出测试集模型得分 score_r = regr.score(X_test,y_test) print(":{}".format(score_r...
min_samples_leaf=20,max_features='sqrt' ,oob_score=True, random_state=10), param_grid = param_test2, scoring='roc_auc',iid=False, cv=5) gsearch2.fit(X,y) gsearch2.grid_scores_, gsearch2.best_params_, gsearch2.best_score_ rf1 = RandomForestClassifier(n_estimators= 60, max_dep...
'__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', '_accumulate', '_acos', '_bisect', '_ceil', '_cos', '_e', '_exp', '_floor', '_inst
X_train, X_test, y_train, y_test = train_test_split(features, targets, test_size=0.2, random_state=42) # 创建随机森林回归模型rf_model = RandomForestRegressor(n_estimators=100, random_state=42) 在训练集上拟合模型 rf_model.fit(X_train, y_train) ...
random_state:int、RandomState 实例或无,默认=无 控制构建树时使用的样本引导的随机性(如果bootstrap=True)和在每个节点寻找最佳分割时要考虑的特征采样(如果max_features < n_features)。有关详细信息,请参阅词汇表。 verbose:整数,默认=0 控制拟合和预测时的详细程度。
cuML - RAPIDS Machine Learning Library. Contribute to rapidsai/cuml development by creating an account on GitHub.
def create_model(): return RandomForestRegressor(min_samples_leaf=2, n_estimators=400, n_jobs=-1, random_state=42) Пример #25 0 Показатьфайл Файл: main.py Проект: LeeMo2K10/GesturePredictor build_auto(DummyRegressor(...
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) 创建RandomForestRegressor模型。 代码语言:txt 复制 rf = RandomForestRegressor() 使用RFECV进行特征选择。 代码语言:txt 复制 rfecv = RFECV(estimator=rf) rfecv.fit(X_train, y_train) ...
(boston.target)) # 22.5328063241106772526x =boston.data27y =boston.target2829#2 分割训练数据和测试数据30#随机采样25%作为测试 75%作为训练31x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=33)3233#3 训练数据和测试数据进行标准化处理34ss_x =...