I tried the sklearn example about grid search ("set parameters by
cross-validation").
The result of gs.best_estimator using the sklearn iris dataset is giving me:
> LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,
> intercept_scaling=1, loss='l2', multi_class='ovr', penalty='l2',
> random_state=None, tol=0.0001, verbose=0)
as opposed to
> LinearSVC(C=0.5, probability=False, degree=3, coef0=0.0, tol=0.001,
> shrinking=True, gamma=0.01)
Also, notice that I had to use gs.best_estimator_, and not gs.best_estimator,
and also that the module name for me is sklearn and not scikits.learn.
Has there been a change in recent versions? And why is the result different?
Thank you,
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