How to use GridSearchCV with RidgeClassifier

Question

I'm trying to use GridSearchCV with RidgeClassifier, but I'm getting this error:

My problem is regression type.

IndexError: too many indices for array

I'm new to Machine Learning, please help me out. This is the code I've been trying to implement:

from sklearn.grid_search import GridSearchCV
from sklearn.metrics import classification_report

tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],
                     'C': [1, 10, 100, 1000]},
                    {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]
scores = ['precision', 'recall']
alphas = np.array([1,0.1,0.01,0.001,0.0001,0])
model = RidgeClassifier(normalize=True, random_state=100, tol=0.1)
for score in scores:
    clf = GridSearchCV(estimator=model, param_grid=dict(alpha=alphas))
    clf.fit(X, Y)
    print("Best parameters set found on development set:")
    print(clf.best_params_)
    for params, mean_score, scores in clf.grid_scores_:
        print("%0.3f (+/-%0.03f) for %r"
              % (mean_score, scores.std() * 2, params))

This is the complete error log:

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-59-c97c7e0fc6f3> in <module>()
     12 for score in scores:
     13     clf = GridSearchCV(estimator=model, param_grid=dict(alpha=alphas))
---> 14     clf.fit(X, Y)
     15     print("Best parameters set found on development set:")
     16     print(clf.best_params_)

/usr/local/lib/python2.7/dist-packages/sklearn/grid_search.pyc in fit(self, X, y)
    836 
    837         """
--> 838         return self._fit(X, y, ParameterGrid(self.param_grid))
    839 
    840 

/usr/local/lib/python2.7/dist-packages/sklearn/grid_search.pyc in _fit(self, X, y, parameter_iterable)
    551                                  'of samples (%i) than data (X: %i samples)'
    552                                  % (len(y), n_samples))
--> 553         cv = check_cv(cv, X, y, classifier=is_classifier(estimator))
    554 
    555         if self.verbose > 0:

/usr/local/lib/python2.7/dist-packages/sklearn/cross_validation.pyc in check_cv(cv, X, y, classifier)
   1833         if classifier:
   1834             if type_of_target(y) in ['binary', 'multiclass']:
-> 1835                 cv = StratifiedKFold(y, cv)
   1836             else:
   1837                 cv = KFold(_num_samples(y), cv)

/usr/local/lib/python2.7/dist-packages/sklearn/cross_validation.pyc in __init__(self, y, n_folds, shuffle, random_state)
    568         for test_fold_idx, per_label_splits in enumerate(zip(*per_label_cvs)):
    569             for label, (_, test_split) in zip(unique_labels, per_label_splits):
--> 570                 label_test_folds = test_folds[y == label]
    571                 # the test split can be too big because we used
    572                 # KFold(max(c, self.n_folds), self.n_folds) instead of

IndexError: too many indices for array

Answer 1

The problem is that Y has shape (15780,1). The shape of Y should show (15780,). The explanation for this is a bit long, so I just give here the links-

Difference between shapes (R,1) and (R,)

Solution for IndexError: too many indices for array