sklearn.datasets.fetch_20newsgroups

sklearn.datasets.fetch_20newsgroups(data_home=None, subset='train', categories=None, shuffle=True, random_state=42, remove=(), download_if_missing=True, return_X_y=False)[source]

Load the filenames and data from the 20 newsgroups dataset (classification).

Download it if necessary.

Classes

20

Samples total

18846

Dimensionality

1

Features

text

Read more in the User Guide.

Parameters
data_homeoptional, default: None

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

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.

categoriesNone or collection of string or unicode

If None (default), load all the categories. If not None, list of category names to load (other categories ignored).

shufflebool, optional

Whether or not to shuffle the data: might be important for models that make the assumption that the samples are independent and identically distributed (i.i.d.), such as stochastic gradient descent.

random_stateint, RandomState instance or None (default)

Determines random number generation for dataset shuffling. Pass an int for reproducible output across multiple function calls. See Glossary.

removetuple

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.

‘headers’ follows an exact standard; the other filters are not always correct.

download_if_missingoptional, 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_yboolean, default=False.

If True, returns (data.data, data.target) instead of a Bunch object.

New in version 0.22.

Returns
bunchBunch object with the following attribute:
  • data: list, length [n_samples]

  • target: array, shape [n_samples]

  • filenames: list, length [n_samples]

  • DESCR: a description of the dataset.

  • target_names: a list of categories of the returned data, length [n_classes]. This depends on the categories parameter.

(data, target)tuple if return_X_y=True

New in version 0.22.