如何在 GridSearchCV(随机森林分类器 Scikit)上获得最佳估计器 [英] How to get Best Estimator on GridSearchCV (Random Forest Classifier Scikit)

问题描述

我正在运行 GridSearch CV 来优化 scikit 中分类器的参数.完成后，我想知道哪些参数被选为最佳.

I'm running GridSearch CV to optimize the parameters of a classifier in scikit. Once I'm done, I'd like to know which parameters were chosen as the best.

每当我这样做时，我都会收到一个 AttributeError: 'RandomForestClassifier' object has no attribute 'best_estimator_'，并且不知道为什么，因为它似乎是 文档.

Whenever I do so I get a AttributeError: 'RandomForestClassifier' object has no attribute 'best_estimator_', and can't tell why, as it seems to be a legitimate attribute on the documentation.

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 '
',CV_rfc.best_estimator_

产量:

`AttributeError: 'GridSearchCV' object has no attribute 'best_estimator_'

推荐答案

必须先拟合数据，然后才能获得最佳参数组合.

You have to fit your data before you can get the best parameter combination.

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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