sklearn.datasets.fetch_20newsgroups_vectorized

sklearn.datasets.fetch_20newsgroups_vectorized(subset=’train’, remove=(), data_home=None, download_if_missing=True)

Load the 20 newsgroups dataset and transform it into tf-idf vectors.

This is a convenience function; the tf-idf transformation is done using the default settings for sklearn.feature_extraction.text.Vectorizer. For more advanced usage (stopword filtering, n-gram extraction, etc.), combine fetch_20newsgroups with a custom Vectorizer or CountVectorizer.

Read more in the User Guide.

Parameters:

subset : ‘train’ or ‘test’, ‘all’, optional

Select the dataset to load: ‘train’ for the training set, ‘test’ for the test set, ‘all’ for both, with shuffled ordering.

remove : tuple

May contain any subset of (‘headers’, ‘footers’, ‘quotes’). Each of these are kinds of text that will be detected and removed from the newsgroup posts, preventing classifiers from overfitting on metadata.

‘headers’ removes newsgroup headers, ‘footers’ removes blocks at the ends of posts that look like signatures, and ‘quotes’ removes lines that appear to be quoting another post.

data_home : optional, default: None

Specify an download and cache folder for the datasets. If None, all scikit-learn data is stored in ‘~/scikit_learn_data’ subfolders.

download_if_missing : optional, True by default

If False, raise an IOError if the data is not locally available instead of trying to download the data from the source site.

Returns:

bunch : Bunch object

bunch.data: sparse matrix, shape [n_samples, n_features] bunch.target: array, shape [n_samples] bunch.target_names: list, length [n_classes]

Examples using sklearn.datasets.fetch_20newsgroups_vectorized

The Johnson-Lindenstrauss bound for embedding with random projections

Model Complexity Influence

Multiclass sparse logisitic regression on newgroups20