lgb_model = lgb.LGBMRegressor( num_leaves=256, reg_alpha=0., reg_lambda=0.01, objective='mae', max_depth=-1, learning_rate=0.03,min_child_samples=25, n_estimators=1200, subsample=0.7, colsample_bytree=0.45,random_state=seed) model=lgb_model.fit(train_x, train['imp']) test_preds+=...
from sklearn.model_selection import train_test_split import numpy as np from sklearn.metrics import roc_auc_score, accuracy_score # 加载数据 iris = datasets.load_iris() # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3) ...
importlightgbmaslgbimportpandasaspdfromsklearn.datasetsimportload_irisfromsklearn.model_selectionimporttrain_test_splitfromsklearn.metricsimportaccuracy_score 1. 2. 3. 4. 5. 加载数据 我们将使用鸢尾花数据集,首先加载数据并进行分割。 AI检测代码解析 # 加载数据data=load_iris()X=pd.DataFrame(data.data...
param = {'max_depth':3,'eta':0.2,'min_child_weight':50} xgb_model = xgb.train(param, dtrain) lgb_model = lgb.train(param, dtrain) xgb可视化 其中num_trees为子树的索引 xgb.to_graphviz(xgb_model, num_trees=0, rankdir='UT') lgb可视化 lgb.create_tree_digraph(model, tree_index=0,...
Will this mount work for the 86’’ QNED80T model? 1 answer Answer this Question LG Digital Care · 2 months ago Hello.The Slim Wall Mount for LG QNED TVs, Model # WB21LGB can be used with the LG 86-inch Class QNED80T Series 4K QNED TV with webOS 24, Model # 86QNED80TUC...
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from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split # 加载数据 iris = load_iris() data = iris.data target = iris.target # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2) ...
importlightgbmaslgbimportnumpyasnpimportpandasaspdfromsklearn.datasetsimportload_bostonfromsklearn.model_selectionimporttrain_test_split# 导入数据boston=load_boston()X=pd.DataFrame(boston.data,columns=boston.feature_names)y=boston.target# 划分训练集与测试集X_train,X_valid,y_train,y_valid=train_test...
importnumpyasnpimportpandasaspdimportxgboostasxgbimporttimefromsklearn.model_selectionimportStratifiedKFoldfromsklearn.model_selectionimporttrain_test_split train_x, train_y, test_x = load_data()# 构建特征# 用sklearn.cross_validation进行训练数据集划分,这里训练集和交叉验证集比例为7:3,可以自己根据需要...