Thank you! one more question. When it comes to pipelining with grid search,
which estimators can I use for feature selection, apart from SVC and PCA?
Thank you,
________________________________
From: Artem [barmaley....@gmail.com]
Sent: Tuesday, April 28, 2015 4:07 PM
To: scikit-learn-general
Subject: Re: [Scikit-learn-general] error with RFE and gridsearchCV
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<mailto: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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