I'm attempting to implement cross validation on the results from my KNN classifier. I have used the following code, which returns a type error.
For context, I have already imported SciKit Learn, Numpy, and Pandas libraries.
from sklearn.cross_validation import cross_val_score, ShuffleSplit n_samples = len(y) knn = KNeighborsClassifier(3) cv = ShuffleSplit(n_samples, n_iter=10, test_size=0.3, random_state=0) test_scores = cross_val_score(knn, X, y, cv=cv) test_scores.mean() Returns:
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-139-d8cc3ee0c29b> in <module>() 7 cv = ShuffleSplit(n_samples, n_iter=10, test_size=0.3, random_state=0) 8 9 test_scores = cross_val_score(knn, X, y, cv=cv) 10 test_scores.mean() //anaconda/lib/python2.7/site-packages/sklearn/cross_validation.pyc in cross_val_score(estimator, X, y, scoring, cv, n_jobs, verbose, fit_params, score_func, pre_dispatch) 1150 delayed(_cross_val_score)(clone(estimator), X, y, scorer, train, test, 1151 verbose, fit_params) 1152 for train, test in cv) 1153 return np.array(scores) 1154 //anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __call__(self, iterable) 515 try: 516 for function, args, kwargs in iterable: 517 self.dispatch(function, args, kwargs) 518 519 self.retrieve() //anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in dispatch(self, func, args, kwargs) 310 """ 311 if self._pool is None: 312 job = ImmediateApply(func, args, kwargs) 313 index = len(self._jobs) 314 if not _verbosity_filter(index, self.verbose): //anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __init__(self, func, args, kwargs) 134 # Don't delay the application, to avoid keeping the input 135 # arguments in memory 136 self.results = func(*args, **kwargs) 137 138 def get(self): //anaconda/lib/python2.7/site-packages/sklearn/cross_validation.pyc in _cross_val_score(estimator, X, y, scorer, train, test, verbose, fit_params) 1056 y_test = None 1057 else: 1058 y_train = y[train] 1059 y_test = y[test] 1060 estimator.fit(X_train, y_train, **fit_params) TypeError: only integer arrays with one element can be converted to an index 