问题
/home/dogus/anaconda2/lib/python2.7/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int8 was converted to float64 by the normalize function. warnings.warn(msg, _DataConversionWarning)
Traceback (most recent call last): File "assignment5.py", line 171, in knn.fit(X_train,y_train) File "/home/dogus/anaconda2/lib/python2.7/site-packages/sklearn/neighbors/base.py", line 775, in fit check_classification_targets(y) File "/home/dogus/anaconda2/lib/python2.7/site-packages/sklearn/utils/multiclass.py", line 172, in check_classification_targets raise ValueError("Unknown label type: %r" % y_type) ValueError: Unknown label type: 'continuous-multioutput'
1-What does 'continuous-multioutput' mean?
2-Is 'DataConversionWarning' something crucial?
# TODO: Just like your preprocessing transformation, create a PCA
# transformation as well. Fit it against your training data, and then
# project your training and testing features into PCA space using the
# PCA model's .transform() method.
pca = PCA(n_components=2, svd_solver='auto')
pca.fit(X_train,y_train)
X_train = pca.transform(X_train)
y_train = pca.transform(y_train)
X_test = pca.transform(X_test) # should i transform tests?
y_test = pca.transform(y_test)
print X_train,"----",y_train
knn = KNeighborsClassifier(n_neighbors=9)
knn.fit(X_train,y_train)
Inputs: print X_train: [[ 0.0669871 0.01377793] [-0.00501622 -0.06383211] ... [ 0.13320158 0.02851528] [-0.06106258 -0.02458237]]
print y_train: [[ 0.02357345 0.01313697] [ 0.02357345 0.01313697] ... [ 0.02357345 0.01313697] [ 0.78585397 0.12070276]]
The main question is how can i solve this continuous-multioutput error? Thank you.
来源：https://stackoverflow.com/questions/42625077/kneighbors-valueerror-continuous-multioutput