如何优化xgboost?
how to optimize xgboost?
我想为 xgboost 分类器执行超参数调整。当我使用特定的超参数值时,我看到了一些错误。请告知调整超参数的正确方法,例如
max_feature、标准、损失等
def xgb_grid_search(X,y,nfolds):
#create a dictionary of all values we want to test
param_grid = {'learning_rate': (0.0001,0.001,0.01,0.05,0.1,0.15)
}
# xgb model
xgb_model=xgb.XGBClassifier()
#use gridsearch to test all values
xgb_gscv = GridSearchCV(xgb_model, param_grid, cv=nfolds)
#fit model to data
xgb_gscv.fit(X, y)
return xgb_gscv.best_params_
不要将您的值括在方括号 ('learning_rate': (0.0001,0.001,0.01,0.05,0.1,0.15)
) 中,而应使用方括号 ('learning_rate': [0.0001,0.001,0.01,0.05,0.1,0.15]
)。这是来自 this kaggle notebook 的示例:
def xgb_grid_search(X,y,nfolds):
#create a dictionary of all values we want to test
param_grid = { 'max_depth': [4,5,6] , 'min_child_weight':[4,5,6] ,'learning_rate': [0.05,0.1,0.5] ,'n_estimators': [20,50,100] }
# decision tree model
xgb_model=XGBClassifier()
#use gridsearch to test all values
xgb_gscv = GridSearchCV(xgb_model, param_grid, cv=nfolds)
#fit model to data
xgb_gscv.fit(X, y)
print(xgb_gscv.best_params_)
print(xgb_gscv.best_estimator_)
print(xgb_gscv.best_score_)
本教程提供了相当全面的指南:https://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/
我想为 xgboost 分类器执行超参数调整。当我使用特定的超参数值时,我看到了一些错误。请告知调整超参数的正确方法,例如 max_feature、标准、损失等
def xgb_grid_search(X,y,nfolds):
#create a dictionary of all values we want to test
param_grid = {'learning_rate': (0.0001,0.001,0.01,0.05,0.1,0.15)
}
# xgb model
xgb_model=xgb.XGBClassifier()
#use gridsearch to test all values
xgb_gscv = GridSearchCV(xgb_model, param_grid, cv=nfolds)
#fit model to data
xgb_gscv.fit(X, y)
return xgb_gscv.best_params_
不要将您的值括在方括号 ('learning_rate': (0.0001,0.001,0.01,0.05,0.1,0.15)
) 中,而应使用方括号 ('learning_rate': [0.0001,0.001,0.01,0.05,0.1,0.15]
)。这是来自 this kaggle notebook 的示例:
def xgb_grid_search(X,y,nfolds):
#create a dictionary of all values we want to test
param_grid = { 'max_depth': [4,5,6] , 'min_child_weight':[4,5,6] ,'learning_rate': [0.05,0.1,0.5] ,'n_estimators': [20,50,100] }
# decision tree model
xgb_model=XGBClassifier()
#use gridsearch to test all values
xgb_gscv = GridSearchCV(xgb_model, param_grid, cv=nfolds)
#fit model to data
xgb_gscv.fit(X, y)
print(xgb_gscv.best_params_)
print(xgb_gscv.best_estimator_)
print(xgb_gscv.best_score_)
本教程提供了相当全面的指南:https://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/