using confusion matrix as scoring metric in cross validation in scikit learn

Question

I am creating a pipeline in scikit learn,

pipeline = Pipeline([     ('bow', CountVectorizer()),       ('classifier', BernoulliNB()),  ]) 

and computing the accuracy using cross validation

scores = cross_val_score(pipeline,  # steps to convert raw messages      into models                      train_set,  # training data                      label_train,  # training labels                      cv=5,  # split data randomly into 10 parts: 9 for training, 1 for scoring                      scoring='accuracy',  # which scoring metric?                      n_jobs=-1,  # -1 = use all cores = faster                      ) 

How can I report confusion matrix instead of 'accuracy'?

Answer 1

You could use cross_val_predict(See the scikit-learn docs) instead of cross_val_score.

instead of doing :

from sklearn.model_selection import cross_val_score scores = cross_val_score(clf, x, y, cv=10) 

you can do :

from sklearn.model_selection import cross_val_predict from sklearn.metrics import confusion_matrix y_pred = cross_val_predict(clf, x, y, cv=10) conf_mat = confusion_matrix(y, y_pred)