.. include:: includes/big_toc_css.rst .. _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 ``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:: modules/pipeline modules/feature_extraction modules/preprocessing modules/unsupervised_reduction modules/random_projection modules/kernel_approximation modules/metrics modules/preprocessing_targets