sklearn.datasets.fetch_20newsgroups_vectorized

sklearn.datasets.fetch_20newsgroups_vectorized(subset=’train’, remove=(), data_home=None, download_if_missing=True, return_X_y=False) [source]

Load the 20 newsgroups dataset and vectorize it into token counts (classification).

Download it if necessary.

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

Classes	20
Samples total	18846
Dimensionality	130107
Features	real

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. return_X_y : boolean, default=False. If True, returns (data.data, data.target) instead of a Bunch object. New in version 0.20.

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] bunch.DESCR: a description of the dataset. (data, target) : tuple if return_X_y is True New in version 0.20.

Examples using sklearn.datasets.fetch_20newsgroups_vectorized