我正在运行GridSearch CV以优化scikit中分类器的参数。完成后,我想知道哪些参数被选为最佳。
每当这样做时,我都会得到一个AttributeError: 'RandomForestClassifier' object has no attribute 'best_estimator_'
,并且无法说出原因,因为它似乎是文档上的合法属性。
from sklearn.grid_search import GridSearchCV
X = data[usable_columns]
y = data[target]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
rfc = RandomForestClassifier(n_jobs=-1,max_features= 'sqrt' ,n_estimators=50, oob_score = True)
param_grid = {
'n_estimators': [200, 700],
'max_features': ['auto', 'sqrt', 'log2']
}
CV_rfc = GridSearchCV(estimator=rfc, param_grid=param_grid, cv= 5)
print '\n',CV_rfc.best_estimator_
产量:
`AttributeError: 'GridSearchCV' object has no attribute 'best_estimator_'
您必须先对数据进行拟合,才能获得最佳的参数组合。
from sklearn.grid_search import GridSearchCV
from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
# Build a classification task using 3 informative features
X, y = make_classification(n_samples=1000,
n_features=10,
n_informative=3,
n_redundant=0,
n_repeated=0,
n_classes=2,
random_state=0,
shuffle=False)
rfc = RandomForestClassifier(n_jobs=-1,max_features= 'sqrt' ,n_estimators=50, oob_score = True)
param_grid = {
'n_estimators': [200, 700],
'max_features': ['auto', 'sqrt', 'log2']
}
CV_rfc = GridSearchCV(estimator=rfc, param_grid=param_grid, cv= 5)
CV_rfc.fit(X, y)
print CV_rfc.best_params_
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