I am getting the following error when i perform classification of new data with the following command in Python:
classifier.predict(new_data)
AttributeError: python 'SVC' object has no attribute _dual_coef_
In my laptop though, the command works fine! What's wrong?
I had this exact error
AttributeError: python 'SVC' object has no attribute _dual_coef_
with a model trained using scikit-learn version 0.15.2, when I tried to run it in scikit-learn version 0.16.1. I did solve it by re-training the model in the latest scikit-learn 0.16.1.
Make sure you are loading the right version of the package.
Have you loaded the model based on which you try to predict? In this case it can be a version conflict, try to re-learn the model using the same sklearn version. You can see a similar problem here: Sklearn error: 'SVR' object has no attribute '_impl'
I had the same problem,I use Sklearn version 0.23.02 but I was trying to run an archive trained with a version 0.18... and my error said: "'SVC' object has no attribute 'break_ties'", I just retrained the model with my version and fix the problem I generate other svc.pickle to run with the 0.23.02 version and replace the oldie.
"""
X = X_train
y = y_train
"""
X = X_test
y = y_test
# Instantiate and train the classifier
from sklearn.neighbors import KNeighborsClassifier
clf = KNeighborsClassifier(n_neighbors=1)
clf.fit(X, y)
# Check the results using metrics
from sklearn import metrics
y_pred = clf.predict(X)
print(metrics.confusion_matrix(y_pred, y))
