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.. _data-transforms:
Dataset transformations
-----------------------
scikit-learn provides a library of transformers, which may clean (see
:ref:`preprocessing`), reduce (see :ref:`data_reduction`), expand (see
:ref:`kernel_approximation`) or generate (see :ref:`feature_extraction`)
feature representations.
Like other estimators, these are represented by classes with a ``fit`` method,
which learns model parameters (e.g. mean and standard deviation for
normalization) from a training set, and a ``transform`` method which applies
this transformation model to unseen data. ``fit_transform`` may be more
convenient and efficient for modelling and transforming the training data
simultaneously.
Combining such transformers, either in parallel or series is covered in
:ref:`combining_estimators`. :ref:`metrics` covers transforming feature
spaces into affinity matrices, while :ref:`preprocessing_targets` considers
transformations of the target space (e.g. categorical labels) for use in
scikit-learn.
.. toctree::
:maxdepth: 2
modules/compose
modules/feature_extraction
modules/preprocessing
modules/impute
modules/unsupervised_reduction
modules/random_projection
modules/kernel_approximation
modules/metrics
modules/preprocessing_targets
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