Python/Scikit-Learn - Can't handle mix of multiclass and continuous

Question

I'm trying to fit an SGDRegressor to my data and then check the accuracy. The fitting works fine, but then the predictions are not in the same datatype(?) as the original target data, and I get the error

ValueError: Can't handle mix of multiclass and continuous 

When calling print "Accuracy:", ms.accuracy_score(y_test,predictions).

The data looks like this (just 200 thousand + rows):

Product_id/Date/product_group1/Price/Net price/Purchase price/Hour/Quantity/product_group2 0   107 12/31/2012  10  300 236 220 10  1   108 

The code is as follows:

from sklearn.preprocessing import StandardScaler import numpy as np from sklearn.linear_model import SGDRegressor import numpy as np from sklearn import metrics as ms  msk = np.random.rand(len(beers)) < 0.8  train = beers[msk] test = beers[~msk]  X = train [['Price', 'Net price', 'Purchase price','Hour','Product_id','product_group2']] y = train[['Quantity']] y = y.as_matrix().ravel()  X_test = test [['Price', 'Net price', 'Purchase price','Hour','Product_id','product_group2']] y_test = test[['Quantity']] y_test = y_test.as_matrix().ravel()  clf = SGDRegressor(n_iter=2000) clf.fit(X, y) predictions = clf.predict(X_test) print "Accuracy:", ms.accuracy_score(y_test,predictions) 

What should I do differently? Thank you!

Answer 1

Accuracy is a classification metric. You can't use it with a regression. See the documentation for info on the various metrics.