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# `sklearn.datasets`.fetch_20newsgroups_vectorized

sklearn.datasets.fetch_20newsgroups_vectorized(*, subset='train', remove=(), data_home=None, download_if_missing=True, return_X_y=False, normalize=True, as_frame=False)

  Load and vectorize the 20 newsgroups dataset (classification).

  Download it if necessary.

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

  The resulting counts are normalized using `sklearn.preprocessing.normalize` unless normalize is set to False.

  Classes | 20
  Samples total | 18846
  Dimensionality | 130107
  Features | real

  Read more in the User Guide.

  Parameters:
    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.

    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.

    data_home str or path-like, 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 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 0.20.

    normalize bool, default=True

      If True, normalizes each document’s feature vector to unit norm using `sklearn.preprocessing.normalize`.

      New in version 0.22.

    as_frame bool, default=False

      If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric, string, or categorical). The target is a pandas DataFrame or Series depending on the number of `target_columns`.

      New in version 0.24.

  Returns:
    bunch `Bunch`

      Dictionary-like object, with the following attributes.

      data: {sparse matrix, dataframe} of shape (n_samples, n_features)

        The input data matrix. If `as_frame` is `True`, `data` is a pandas DataFrame with sparse columns.

      target: {ndarray, series} of shape (n_samples,)

        The target labels. If `as_frame` is `True`, `target` is a pandas Series.

      target_names: list of shape (n_classes,)

        The names of target classes.

      DESCR: str

        The full description of the dataset.

      frame: dataframe of shape (n_samples, n_features + 1)

        Only present when `as_frame=True`. Pandas DataFrame with `data` and `target`.

        New in version 0.24.

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

      `data` and `target` would be of the format defined in the `Bunch` description above.

      New in version 0.20.

## Examples using `sklearn.datasets.fetch_20newsgroups_vectorized`

Model Complexity Influence

Model Complexity Influence

Multiclass sparse logistic regression on 20newgroups

Multiclass sparse logistic regression on 20newgroups

The Johnson-Lindenstrauss bound for embedding with random projections

The Johnson-Lindenstrauss bound for embedding with random projections