我想打印精度,回想一下Grid中使用的每个参数,如何做到这一点。
我的Gridsearch代码
from sklearn.grid_search import GridSearchCV
rf1=RandomForestClassifier(n_jobs=-1, max_features='sqrt')
#fit_rf1=rf.fit(X_train_res,y_train_res)
# Use a grid over parameters of interest
param_grid = {
"n_estimators" : [50, 100, 150, 200],
"max_depth" : [2, 5, 10],
"min_samples_leaf" : [10,20,30]}
from sklearn.metrics import make_scorer
from sklearn.metrics import precision_score,recall_score
scoring = {'precision': make_scorer(precision_score), 'Recall': make_scorer(recall_score)}
CV_rfc = GridSearchCV(estimator=rf1, param_grid=param_grid, cv= 10,scoring=scoring)
CV_rfc.fit(X_train_res, y_train_res)
我的预期输出
{'max_depth': 10, 'min_samples_leaf': 2, 'n_estimators': 50,'accuracy':.97,'recall':.89}
{'max_depth': 5, 'min_samples_leaf':10 , 'n_estimators': 100,'accuracy':.98,'recall':.92}
如果您将scoring
设置为得分者列表,您可以获得CV_rfc.cv_results_
中每位得分手的平均得分。
例如:
from sklearn.datasets import make_classification
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
X, y = make_classification()
base_clf = RandomForestClassifier()
param_grid = {
"n_estimators" : [50, 100, 150, 200],}
CV_rf = GridSearchCV(base_clf, param_grid, scoring=['accuracy', 'roc_auc'], refit=False)
CV_rf.fit(X, y)
print(CV_rf.cv_results_)
你得到的输出如下:
{'mean_fit_time': array([ 0.05867839, 0.10268728, 0.15536443, 0.19937317]),
'mean_score_time': array([ 0.00600123, 0.01033529, 0.0146695 , 0.02000403]),
'mean_test_accuracy': array([ 0.9 , 0.91, 0.89, 0.91]),
'mean_test_roc_auc': array([ 0.91889706, 0.94610294, 0.94253676, 0.94308824]),
'mean_train_accuracy': array([ 1., 1., 1., 1.]),
'mean_train_roc_auc': array([ 1., 1., 1., 1.]),
[...]
}
所以mean_test_[scoring]
就是你追求的。请注意,您可以将cv_results_
导入为Pandas DataFrame。这有助于提高可读性!