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scikit-learn 1.3.2
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# `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)

  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_home str or path-like, 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’, ‘test’, ‘all’}, default=’train’

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

    categories array-like, dtype=str, default=None

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

    shuffle bool, default=True

      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_state int, RandomState instance or None, default=42

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

    remove tuple, default=()

      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_missing bool, default=True

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

    return_X_y bool, default=False

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

      New in version 0.22.

  Returns:
    bunch `Bunch`

      Dictionary-like object, with the following attributes.

      datalist of shape (n_samples,)

        The data list to learn.

      target: ndarray of shape (n_samples,)

        The target labels.

      filenames: list of shape (n_samples,)

        The path to the location of the data.

      DESCR: str

        The full description of the dataset.

      target_names: list of shape (n_classes,)

        The names of target classes.

    (data, target)tuple if `return_X_y=True`

      A tuple of two ndarrays. The first contains a 2D array of shape (n_samples, n_classes) with each row representing one sample and each column representing the features. The second array of shape (n_samples,) contains the target samples.

      New in version 0.22.

## Examples using `sklearn.datasets.fetch_20newsgroups`

Biclustering documents with the Spectral Co-clustering algorithm

Biclustering documents with the Spectral Co-clustering algorithm

Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation

Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation

Sample pipeline for text feature extraction and evaluation

Sample pipeline for text feature extraction and evaluation

Column Transformer with Heterogeneous Data Sources

Column Transformer with Heterogeneous Data Sources

Semi-supervised Classification on a Text Dataset

Semi-supervised Classification on a Text Dataset

Classification of text documents using sparse features

Classification of text documents using sparse features

Clustering text documents using k-means

Clustering text documents using k-means

FeatureHasher and DictVectorizer Comparison

FeatureHasher and DictVectorizer Comparison