data_transforms.rst

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