如何在GridSearchCV（随机森林分类器Scikit）上获得最佳估计器

Question

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_'

Answer 1

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_