Suppose we have a dataset that has been split into train and test sets $($so we have train$\_$features, train$\_$labels, test$\_$features, test$\_$labels$).$ We want to run a perceptron algorithm that classifies unseen instances.

$$

Which of the following pipelines correctly demonstrates this?

Aclassifier $=$ sklearn.linear$\_$model.Perceptron$()$

classifier.fit$($train$\_$features, train$\_$labels$)$

classifier.predict$($test$\_$features$)$Bclassifier $=$ sklearn.linear$\_$model.Perceptron$()$

classifier.fit$($train$\_$features, train$\_$labels$)$

classifier.predict$($train$\_$features$)$Cclassifier $=$ sklearn.linear$\_$model.Perceptron$()$

classifier.fit$($test$\_$features, test$\_$labels$)$

classifier.predict$($test$\_$features$)$Dclassifier $=$ sklearn.linear$\_$model.Perceptron$()$

classifier.fit$($train$\_$features, train$\_$labels$)$

classifier.predict$($train$\_$labels$)$

$$

Group of answer choices

A

B

C

D