代码之家 › 专栏 › 技术社区 › stone rock

LightGBM错误-长度与数据不同

lightgbm machine-learning

1

stone rock · 技术社区 · 7 年前

我正在使用lightgbm查找功能重要性,但我遇到了错误 lightgbm error:b'len of label is not same with data'. 。 X.形状 (73147、12) Y形 (73147,)


   
    
     代码:
    
    来自sklearn.model_selection import train_test_split
将lightgbm导入为lgb

#初始化空数组以保存功能导入
特征输入=np.zeros(x.shape[1])

#使用多个超参数创建模型
model=lgb.lgbmClassifier(objective='binary',boosting'goss',n'u estimators=10000,class'weight='balanced')

#安装模型两次以避免过度安装
对于范围(2)中的i:

#分为培训和验证集
x_-train,x_-test,y_-train,y_-test=train_-test_-split(x,y,test_-size=0.25,random_-state=i)

#列车使用早停
模型.拟合(x,y_列车,早期停止轮数=100,eval_设置=[(x_测试,y_测试)]
eval_metric='auc',verbose=200)

#记录特征导入
feature_importances+=型号。feature_importances_
请参见下面的屏幕截图:

.

代码:


from sklearn.model_selection import train_test_split
import lightgbm as lgb

# Initialize an empty array to hold feature importances
feature_importances = np.zeros(X.shape[1])

# Create the model with several hyperparameters
model = lgb.LGBMClassifier(objective='binary', boosting_type = 'goss', n_estimators = 10000, class_weight = 'balanced')

# Fit the model twice to avoid overfitting
for i in range(2):

    # Split into training and validation set
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = i)

    # Train using early stopping
    model.fit(X, y_train, early_stopping_rounds=100, eval_set = [(X_test, y_test)], 
              eval_metric = 'auc', verbose = 200)

    # Record the feature importances
    feature_importances += model.feature_importances_


请参见下面的屏幕截图:

1 回复 | 直到 7 年前

1

desertnaut SKZI 7 年前

model.fit(X, y_train, [...])

model.fit(X_train, y_train, [...])

X 和 y_train