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- y = data['Sort'].astype(float)
- X = data.drop('Sort', axis=1)
- X = data.astype(float)
- X_scale = sk.preprocessing.scale(X)
- y_scale = sk.preprocessing.scale(y)
- kMeans_scale = []
- for k in range(1,51):
- kn = KNeighborsClassifier(n_neighbors=k)
- kn.fit(X_scale, y_scale)
- array = cross_val_score(estimator = kn, X=X_scale, y=y_scale, cv=kf, scoring = 'accuracy')
- kMeans_scale.append(m)
- m = max(kMeans_scale)
- print(np.round(m,decimals = 2))
- s = kMeans_scale.index(m)
- print(s+1)
- ValueError Traceback (most recent call last)
- <ipython-input-134-af5b3598c259> in <module>
- 2 for k in range(1,51):
- 3 kn = KNeighborsClassifier(n_neighbors=k)
- ----> 4 kn.fit(X_scale, y_scale)
- 5 array = cross_val_score(estimator = kn, X=X_scale, y=y_scale, cv=kf, scoring = 'accuracy')
- 6 kMeans_scale.append(m)
- ~Anaconda3libsite-packagessklearnneighborsbase.py in fit(self, X, y)
- 903 self.outputs_2d_ = True
- 904
- --> 905 check_classification_targets(y)
- 906 self.classes_ = []
- 907 self._y = np.empty(y.shape, dtype=np.int)
- ~Anaconda3libsite-packagessklearnutilsmulticlass.py in check_classification_targets(y)
- 169 if y_type not in ['binary', 'multiclass', 'multiclass-multioutput',
- 170 'multilabel-indicator', 'multilabel-sequences']:
- --> 171 raise ValueError("Unknown label type: %r" % y_type)
- 172
- 173
- ValueError: Unknown label type: 'continuous'
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