While True: Learn() Download



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

Download it if necessary.

Learn()

This is a convenience function; the transformation is done using thedefault settings forsklearn.feature_extraction.text.CountVectorizer. For moreadvanced usage (stopword filtering, n-gram extraction, etc.), combinefetch_20newsgroups with a customsklearn.feature_extraction.text.CountVectorizer,sklearn.feature_extraction.text.HashingVectorizer,sklearn.feature_extraction.text.TfidfTransformer orsklearn.feature_extraction.text.TfidfVectorizer.

The resulting counts are normalized usingsklearn.preprocessing.normalize unless normalize is set to False.

Classes

20

Samples total

18846

Dimensionality

130107

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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.

removetuple
While True: Learn() Download

May contain any subset of (‘headers’, ‘footers’, ‘quotes’). Each ofthese are kinds of text that will be detected and removed from thenewsgroup posts, preventing classifiers from overfitting onmetadata.

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

data_homeoptional, 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_missingoptional, True by default

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

return_X_ybool, default=False

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

normalizebool, default=True

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

New in version 0.22.

Returns
bunchBunch

Dictionary-like object, with the following attributes.

data: sparse matrix, shape [n_samples, n_features]

The data matrix to learn.

target: array, shape [n_samples]

The target labels.

While True Learn() Download

target_names: list, length [n_classes]

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The names of target classes.

DESCR: str

The full description of the dataset.

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(data, target)tuple if return_X_y is True

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