Let's now simply replicate the simple logistic regression we did in Chapter 1, Getting Started with Text Classification, but on our custom dataset, as follows:
from sklearn.linear_model import LogisticRegression as LRlr_clf = Pipeline([('vect', CountVectorizer()), ('tfidf', TfidfTransformer()), ('clf',LR())])
As you can see in the preceding snippet, lr_clf becomes our classifier pipeline. We saw the pipeline in our introductory section. A pipeline allows us to queue multiple operations in one single Python object.
We are able to call functions such as fit, predict, and fit_transform on our Pipeline objects because a pipeline automatically calls the corresponding function of the last component in the list.
lr_clf.fit(X=X_train, ...
