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