在机器学习库sklearn中,构建模型、生成随机数据集、拆分数据集时经常会看到random_state这个参数,例如: data = make_blobs(n_samples=100, centers =2,random_state=9)//生成数据集时 X, y = make_regression(n_features=1,n_informative=1,noise=30,random_state=5)//构建模型 x_train, x_test, y_tra...
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...
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)...
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, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=...
train_x, valid_x, train_y, valid_y = train_test_split(x, y, test_size=0.333, random_state=0) # 分训练集和验证集 train = lgb.Dataset(train_x, train_y) valid = lgb.Dataset(valid_x, valid_y, reference=train) parameters = { ...
如果没有看到拟合模型所依据的数据，就很难判断。但是，在错误和代码之间，看起来可能是在67个特征的...
(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 =...
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) ...
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) ...
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