doc/data_transforms.rst
.. _data-transforms:
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