通过def函数运行模型
通过def函数运行模型
提问于 2020-06-06 03:53:15
我有一组3个gredi估计器这样调用它们，但我不喜欢它们，我想通过循环调用它们。但是我的函数不起作用。
from sklearn.model_selection import GridSearchCV
##==============================================================================
RFC1 = GridSearchCV(estimator=RFC,
param_grid = params_RFC1,
cv=cv_method,
verbose=1,
scoring = 'roc_auc')
##==============================================================================
RFC2 = GridSearchCV(estimator=RFC,
param_grid = params_RFC2,
cv=cv_method,
verbose=1,
scoring = 'roc_auc')
##==============================================================================
RFC3 = GridSearchCV(estimator=RFC,
param_grid = params_RFC3,
cv=cv_method,
verbose=1,
scoring = 'roc_auc')
##==============================================================================我犯了一个错误，我不知道在哪里
from sklearn.model_selection import GridSearchCV
global model
def model(estimator,param_grid,model):
model = GridSearchCV(estimator=estimator,
param_grid = params,
cv=cv_method,
verbose=1,
scoring = 'roc_auc')
return model回答 1
Stack Overflow用户
发布于 2020-06-06 05:34:42
from sklearn.ensemble import RandomForestClassifier
RFC = RandomForestClassifier()
#Cross validation
from sklearn.model_selection import RepeatedStratifiedKFold
cv_method = RepeatedStratifiedKFold(n_splits=5,
n_repeats=3,
random_state=999)
params_RFC2 = {
'max_depth': [2, 3],
'min_samples_leaf': [3, 4],
'n_estimators': [500,1000]}
gs_RFC = GridSearchCV(estimator=RFC,
param_grid = params_RFC,
cv=cv_method,
verbose=1,
scoring = 'roc_auc')
gs_RFC.fit(Data, target)页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/62223203
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