66 lines
2.5 KiB
ReStructuredText
66 lines
2.5 KiB
ReStructuredText
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.. _datasets:
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=========================
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Dataset loading utilities
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=========================
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.. currentmodule:: sklearn.datasets
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The ``sklearn.datasets`` package embeds some small toy datasets
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as introduced in the :ref:`Getting Started <loading_example_dataset>` section.
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This package also features helpers to fetch larger datasets commonly
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used by the machine learning community to benchmark algorithms on data
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that comes from the 'real world'.
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To evaluate the impact of the scale of the dataset (``n_samples`` and
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``n_features``) while controlling the statistical properties of the data
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(typically the correlation and informativeness of the features), it is
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also possible to generate synthetic data.
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**General dataset API.** There are three main kinds of dataset interfaces that
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can be used to get datasets depending on the desired type of dataset.
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**The dataset loaders.** They can be used to load small standard datasets,
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described in the :ref:`toy_datasets` section.
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**The dataset fetchers.** They can be used to download and load larger datasets,
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described in the :ref:`real_world_datasets` section.
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Both loaders and fetchers functions return a :class:`~sklearn.utils.Bunch`
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object holding at least two items:
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an array of shape ``n_samples`` * ``n_features`` with
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key ``data`` (except for 20newsgroups) and a numpy array of
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length ``n_samples``, containing the target values, with key ``target``.
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The Bunch object is a dictionary that exposes its keys as attributes.
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For more information about Bunch object, see :class:`~sklearn.utils.Bunch`.
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It's also possible for almost all of these function to constrain the output
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to be a tuple containing only the data and the target, by setting the
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``return_X_y`` parameter to ``True``.
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The datasets also contain a full description in their ``DESCR`` attribute and
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some contain ``feature_names`` and ``target_names``. See the dataset
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descriptions below for details.
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**The dataset generation functions.** They can be used to generate controlled
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synthetic datasets, described in the :ref:`sample_generators` section.
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These functions return a tuple ``(X, y)`` consisting of a ``n_samples`` *
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``n_features`` numpy array ``X`` and an array of length ``n_samples``
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containing the targets ``y``.
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In addition, there are also miscellaneous tools to load datasets of other
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formats or from other locations, described in the :ref:`loading_other_datasets`
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section.
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.. toctree::
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:maxdepth: 2
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datasets/toy_dataset
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datasets/real_world
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datasets/sample_generators
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datasets/loading_other_datasets
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