Evaluating model with categorical target variables I converted all the numeric target variables of MNIST dataset into categorical variables. So, 0 became zero, and so on. Next, I tried the following model on this dataset:
from sklearn.linear_model import SGDClassifier
from sklearn.multiclass import OneVsRestClassifier

sgd_clf = OneVsRestClassifier(SGDClassifier(random_state=42))
sgd_clf.fit(X_train, y_train)

When I try sgd_clf.predict(X_test), I get an array of categorical values. I want to understand how to evaluate my model since confusion_matrix is not working on this.
You could convert your categorical outputs back into their numeric equivalents, and then use the confusion matrix and normal