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)[source]¶
Load the filenames and data from the 20 newsgroups dataset.
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.
data_home: optional, default: None :
Specify a download and cache folder for the datasets. If None, all scikit-learn data is stored in ‘~/scikit_learn_data’ subfolders.
categories: None 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).
shuffle: bool, 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_state: numpy random number generator or seed integer :
Used to shuffle the dataset.
download_if_missing: optional, 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.
remove: tuple :
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.
