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) ...
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,...
lgb训练代码 importlightgbmaslgbimportpandasaspdfromsklearn.metricsimportconfusion_matrix,accuracy_score,precision_score,recall_score,f1_scorefromsklearn.utilsimportresampleimportpickleimporttimeimportwarningswarnings.filterwarnings('ignore')if__name__=="__main__":print("---训练开始---")start_time=time...
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) ...
The XXL II decoder features selectable 14/28/128 speed steps, 3 light outputs and 10 function outputs with a maximum total load capacity of 2.0Amps, 1 contact input, 2 servo controls, analog and digital operation, digital and analog Back EMF, and a Massoth-SUSI interface. ...
from sklearn.model_selection import train_test_split print("Loading Data ... ") # 导入数据 train_x, train_y, test_x = load_data() #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,可以自己根据需要设置 ...
model_selection import train_test_split print("Loading Data ... ") # 导入数据 train_x, train_y, test_x = load_data() #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,可以自己根据需要设置 X, val_X, y, val_y = train_test_split( train_x, train_y, ...
astype(int) #下面是使用手写的xgboost对一个示例数据集进行训练和预测的代码: data = load_breast_cancer() X = data.data y = data.target model = XGBoost() model.fit(X, y) y_pred = model.predict(X) print("Accuracy:", accuracy_score(y, np.round(y_pred))) #运行结果如下: #Accuracy:...
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。 # 加载数据data=load_iris()X=pd.DataFrame(data.data,columns=data....