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DOC add entries missing from glossary
With thanks to the demo of numpy/numpydoc#150
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doc/glossary.rst

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@@ -149,6 +149,12 @@ General Concepts
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introduces changes that are not backwards compatible, these are known
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as software regressions.
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callable
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A function, class or an object which implements the ``__call__``
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method; anything that returns True when the argument of
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`https://docs.python.org/3/library/functions.html#callable
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<callable()>`_.
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categorical feature
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A categorical or nominal :term:`feature` is one that has a
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finite set of discrete values across the population of data.
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hyper-parameter
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See :term:`parameter`.
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indexable
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An :term:`array-like`, :term:`sparse matrix`, pandas DataFrame or
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sequence (usually a list).
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induction
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inductive
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Inductive (contrasted with :term:`transductive`) machine learning
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efficiently working with numpy arrays, such as through use of
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:term:`memory mapping`.
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label indicator matrix
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multilabel indicator matrix
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The format used to represent multilabel data, where each row of a 2d
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array or sparse matrix corresponds to a sample, each column
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corresponds to a class, and each element is 1 if the sample is labeled
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with the class and 0 if not.
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leakage
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data leakage
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A problem in cross validation where generalization performance can be
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*tasks* or *targets*.
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See :term:`multiclass multioutput` and :term:`continuous multioutput`.
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pair
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A tuple of length two.
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parameter
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parameters
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param
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The sparse matrix is interpreted as an array with implicit and
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explicit zeros being interpreted as the number 0. This is the
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interpretation most often adopted, e.g. when sparse matrices
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are used for feature matrices or multilabel indicator matrices.
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are used for feature matrices or :term:`multilabel indicator
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matrices`.
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graph semantics
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As with :mod:`scipy.sparse.csgraph`, explicit zeros are
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interpreted as the number 0, but implicit zeros indicate a masked
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.. glossary:
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cross validation splitter
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cross-validation splitter
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CV splitter
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cross-validation generator
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A non-estimator family of classes used to split a dataset into a
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sequence of train and test portions (see :ref:`cross_validation`),
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by providing :term:`split` and :term:`get_n_splits` methods.
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:term:`targets` may represent a binary or multiclass (but not
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multioutput) classification problem (determined by
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:func:`utils.multiclass.type_of_target`).
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- A :term:`cross validation splitter` instance. Refer to the
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- A :term:`cross-validation splitter` instance. Refer to the
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:ref:`User Guide <cross_validation>` for splitters available
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within Scikit-learn.
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- An iterable yielding train/test splits.

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