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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 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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Read more in the User Guide.
Select the dataset to load: ‘train’ for the training set, ‘test’for the test set, ‘all’ for both, with shuffled ordering.
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.
Specify an download and cache folder for the datasets. If None,all scikit-learn data is stored in ‘~/scikit_learn_data’ subfolders.
If False, raise an IOError if the data is not locally availableinstead of trying to download the data from the source site.
If True, returns (data.data,data.target)
instead of a Bunchobject.
If True, normalizes each document’s feature vector to unit norm usingsklearn.preprocessing.normalize
.
New in version 0.22.
Bunch
Dictionary-like object, with the following attributes.
The data matrix to learn.
The target labels.
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The names of target classes.
The full description of the dataset.
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