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