Python Machine Learning By Example

News topic classification with support vector machine

It is finally time to build our state-of-the-art, SVM-based news topic classifier with all we just learned.

Load and clean the news dataset with the whole 20 groups:

>>> categories = None>>> data_train = fetch_20newsgroups(subset='train',                            categories=categories, random_state=42)>>> data_test = fetch_20newsgroups(subset='test',                            categories=categories, random_state=42)>>> cleaned_train = clean_text(data_train.data)>>> label_train = data_train.target>>> cleaned_test = clean_text(data_test.data)>>> label_test = data_test.target>>> term_docs_train =                   tfidf_vectorizer.fit_transform(cleaned_train)>>> term_docs_test = tfidf_vectorizer.transform(cleaned_test)

Recall that the linear kernel is ...