GridSearchCV is not an estimator, but an "utility" to find one. So you
should `fit` grid search first in order to find that classifier that
performs well on cv-splits, and then use it. Like this
gbr = GradientBoostingClassifier()
parameters = {'learning_rate': [0.1, 0.01, 0.001],
'max_depth': [1, 4, 6],
'min_samples_leaf': [3, 5, 9, 17],
'max_features': [1.0, 0.3, 0.1]}
clf = grid_search.GridSearchCV(estimator=gbr, param_grid=parameters,
n_jobs=16)
*clf.fit(x_train, y_train)*
rfecv = RFECV(estimator=clf*.best_estimator_*, step=1, cv=10,
scoring='accuracy')
rfecv.fit(x_train, y_train)
# prediction
y_predicted = rfecv.estimator_.predict(x_test)
Also
, note that RFECV
<http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html>
only supports
models that have coef_
attribute, and GradientBoostingClassifier does not.
On Tue, Apr 28, 2015 at 8:44 PM, Pagliari, Roberto <rpagli...@appcomsci.com>
wrote:
> I'm trying to use recursive feature elimination with gradient boosting
> and grid search as shown below
>
>
> gbr = GradientBoostingClassifier()
> parameters = {'learning_rate': [0.1, 0.01, 0.001],
> 'max_depth': [1, 4, 6],
> 'min_samples_leaf': [3, 5, 9, 17],
> 'max_features': [1.0, 0.3, 0.1]}
> clf = grid_search.GridSearchCV(estimator=gbr, param_grid=parameters,
> n_jobs=16)
> rfecv = RFECV(estimator=clf, step=1, cv=10, scoring='accuracy')
> rfecv.fit(x_train, y_train)
>
> # prediction
> y_predicted = rfecv.estimator_.predict(x_test)
>
> However, I'm getting this error and I don't know how to fix it:
>
> Traceback (most recent call last):
> File "./gbr_rfe.py", line 92, in <module>
> rfecv.fit(x_train, y_train)
> File
> "/usr/local/lib/python2.7/dist-packages/sklearn/feature_selection/rfe.py",
> line 376, in fit
> ranking_ = rfe.fit(X_train, y_train).ranking_
> File
> "/usr/local/lib/python2.7/dist-packages/sklearn/feature_selection/rfe.py",
> line 163, in fit
> if estimator.coef_.ndim > 1:
> AttributeError: 'GridSearchCV' object has no attribute 'coef_'
>
>
>
>
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