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7.. _data-transforms:
8
9Dataset transformations
10-----------------------
11
12scikit-learn provides a library of transformers, which may clean (see
13:ref:`preprocessing`), reduce (see :ref:`data_reduction`), expand (see
14:ref:`kernel_approximation`) or generate (see :ref:`feature_extraction`)
15feature representations.
16
17Like other estimators, these are represented by classes with a ``fit`` method,
18which learns model parameters (e.g. mean and standard deviation for
19normalization) from a training set, and a ``transform`` method which applies
20this transformation model to unseen data. ``fit_transform`` may be more
21convenient and efficient for modelling and transforming the training data
22simultaneously.
23
24Combining such transformers, either in parallel or series is covered in
25:ref:`combining_estimators`. :ref:`metrics` covers transforming feature
26spaces into affinity matrices, while :ref:`preprocessing_targets` considers
27transformations of the target space (e.g. categorical labels) for use in
28scikit-learn.
29
30.. toctree::
31    :maxdepth: 2
32
33    modules/compose
34    modules/feature_extraction
35    modules/preprocessing
36    modules/impute
37    modules/unsupervised_reduction
38    modules/random_projection
39    modules/kernel_approximation
40    modules/metrics
41    modules/preprocessing_targets
42