I want to do a grid search that does two things at once: chooses the right
value for C, the regularization parameter, and does feature selection with
recursive feature elimination.
As a reminder, here's how you usually use the Recursive Feature Elimination
Cross Validation (RFECV) method:
from sklearn.feature_selection import RFECV
from sklearn.svm import SVR
estimator = SVR(kernel="linear", C=100)
selector = RFECV(estimator, step=1, cv=5)
selector = selector.fit(X, y)
I also I tried the following:
parameters = {"estimator": (LogisticRegression(C=100)
, LogisticRegression(C=1000))}
clf = GridSearchCV(RFECV(step=1 cv=StratifiedKFold(y, 2)),
loss_func=zero_one)
clf.fit(X, y)
Unsurprisingly, the above code doesn't work because it's not possible to
initialize a RFECV object without an estimator. But I can't pass it an
estimator yet because I want to vary the value of C that is used in
initializing the estimator. Can someone show me how this is done?
Thanks,
Conrad
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