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<section id="glossary-of-common-terms-and-api-elements">
<span id="glossary"></span><h1>Glossary of Common Terms and API Elements<a class="headerlink" href="#glossary-of-common-terms-and-api-elements" title="Link to this heading">#</a></h1>
<p>This glossary hopes to definitively represent the tacit and explicit
conventions applied in Scikit-learn and its API, while providing a reference
for users and contributors. It aims to describe the concepts and either detail
their corresponding API or link to other relevant parts of the documentation
which do so. By linking to glossary entries from the API Reference and User
Guide, we may minimize redundancy and inconsistency.</p>
<p>We begin by listing general concepts (and any that didn’t fit elsewhere), but
more specific sets of related terms are listed below:
<a class="reference internal" href="#glossary-estimator-types"><span class="std std-ref">Class APIs and Estimator Types</span></a>, <a class="reference internal" href="#glossary-target-types"><span class="std std-ref">Target Types</span></a>,
<a class="reference internal" href="#glossary-methods"><span class="std std-ref">Methods</span></a>, <a class="reference internal" href="#glossary-parameters"><span class="std std-ref">Parameters</span></a>,
<a class="reference internal" href="#glossary-attributes"><span class="std std-ref">Attributes</span></a>, <a class="reference internal" href="#glossary-sample-props"><span class="std std-ref">Data and sample properties</span></a>.</p>
<section id="general-concepts">
<h2>General Concepts<a class="headerlink" href="#general-concepts" title="Link to this heading">#</a></h2>
<dl class="glossary">
<dt id="term-1d">1d<a class="headerlink" href="#term-1d" title="Link to this term">#</a></dt><dt id="term-1d-array">1d array<a class="headerlink" href="#term-1d-array" title="Link to this term">#</a></dt><dd><p>One-dimensional array. A NumPy array whose <code class="docutils literal notranslate"><span class="pre">.shape</span></code> has length 1.
A vector.</p>
</dd>
<dt id="term-2d">2d<a class="headerlink" href="#term-2d" title="Link to this term">#</a></dt><dt id="term-2d-array">2d array<a class="headerlink" href="#term-2d-array" title="Link to this term">#</a></dt><dd><p>Two-dimensional array. A NumPy array whose <code class="docutils literal notranslate"><span class="pre">.shape</span></code> has length 2.
Often represents a matrix.</p>
</dd>
<dt id="term-API">API<a class="headerlink" href="#term-API" title="Link to this term">#</a></dt><dd><p>Refers to both the <em>specific</em> interfaces for estimators implemented in
Scikit-learn and the <em>generalized</em> conventions across types of
estimators as described in this glossary and <a class="reference internal" href="developers/develop.html#api-overview"><span class="std std-ref">overviewed in the
contributor documentation</span></a>.</p>
<p>The specific interfaces that constitute Scikit-learn’s public API are
largely documented in <a class="reference internal" href="api/index.html#api-ref"><span class="std std-ref">API Reference</span></a>. However, we less formally consider
anything as public API if none of the identifiers required to access it
begins with <code class="docutils literal notranslate"><span class="pre">_</span></code>. We generally try to maintain <a class="reference internal" href="#term-backwards-compatibility"><span class="xref std std-term">backwards
compatibility</span></a> for all objects in the public API.</p>
<p>Private API, including functions, modules and methods beginning <code class="docutils literal notranslate"><span class="pre">_</span></code>
are not assured to be stable.</p>
</dd>
<dt id="term-array-like">array-like<a class="headerlink" href="#term-array-like" title="Link to this term">#</a></dt><dd><p>The most common data format for <em>input</em> to Scikit-learn estimators and
functions, array-like is any type object for which
<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.asarray.html#numpy.asarray" title="(in NumPy v2.2)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.asarray</span></code></a> will produce an array of appropriate shape
(usually 1 or 2-dimensional) of appropriate dtype (usually numeric).</p>
<p>This includes:</p>
<ul class="simple">
<li><p>a numpy array</p></li>
<li><p>a list of numbers</p></li>
<li><p>a list of length-k lists of numbers for some fixed length k</p></li>
<li><p>a <a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.3)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a> with all columns numeric</p></li>
<li><p>a numeric <a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.2.3)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.Series</span></code></a></p></li>
</ul>
<p>It excludes:</p>
<ul class="simple">
<li><p>a <a class="reference internal" href="#term-sparse-matrix"><span class="xref std std-term">sparse matrix</span></a></p></li>
<li><p>a sparse array</p></li>
<li><p>an iterator</p></li>
<li><p>a generator</p></li>
</ul>
<p>Note that <em>output</em> from scikit-learn estimators and functions (e.g.
predictions) should generally be arrays or sparse matrices, or lists
thereof (as in multi-output <a class="reference internal" href="modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier" title="sklearn.tree.DecisionTreeClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">tree.DecisionTreeClassifier</span></code></a>’s
<code class="docutils literal notranslate"><span class="pre">predict_proba</span></code>). An estimator where <code class="docutils literal notranslate"><span class="pre">predict()</span></code> returns a list or
a <code class="docutils literal notranslate"><span class="pre">pandas.Series</span></code> is not valid.</p>
</dd>
<dt id="term-attribute">attribute<a class="headerlink" href="#term-attribute" title="Link to this term">#</a></dt><dt id="term-attributes">attributes<a class="headerlink" href="#term-attributes" title="Link to this term">#</a></dt><dd><p>We mostly use attribute to refer to how model information is stored on
an estimator during fitting. Any public attribute stored on an
estimator instance is required to begin with an alphabetic character
and end in a single underscore if it is set in <a class="reference internal" href="#term-fit"><span class="xref std std-term">fit</span></a> or
<a class="reference internal" href="#term-partial_fit"><span class="xref std std-term">partial_fit</span></a>. These are what is documented under an estimator’s
<em>Attributes</em> documentation. The information stored in attributes is
usually either: sufficient statistics used for prediction or
transformation; <a class="reference internal" href="#term-transductive"><span class="xref std std-term">transductive</span></a> outputs such as <a class="reference internal" href="#term-labels_"><span class="xref std std-term">labels_</span></a> or
<a class="reference internal" href="#term-embedding_"><span class="xref std std-term">embedding_</span></a>; or diagnostic data, such as
<a class="reference internal" href="#term-feature_importances_"><span class="xref std std-term">feature_importances_</span></a>.
Common attributes are listed <a class="reference internal" href="#glossary-attributes"><span class="std std-ref">below</span></a>.</p>
<p>A public attribute may have the same name as a constructor
<a class="reference internal" href="#term-parameter"><span class="xref std std-term">parameter</span></a>, with a <code class="docutils literal notranslate"><span class="pre">_</span></code> appended. This is used to store a
validated or estimated version of the user’s input. For example,
<a class="reference internal" href="modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="sklearn.decomposition.PCA"><code class="xref py py-class docutils literal notranslate"><span class="pre">decomposition.PCA</span></code></a> is constructed with an <code class="docutils literal notranslate"><span class="pre">n_components</span></code>
parameter. From this, together with other parameters and the data,
PCA estimates the attribute <code class="docutils literal notranslate"><span class="pre">n_components_</span></code>.</p>
<p>Further private attributes used in prediction/transformation/etc. may
also be set when fitting. These begin with a single underscore and are
not assured to be stable for public access.</p>
<p>A public attribute on an estimator instance that does not end in an
underscore should be the stored, unmodified value of an <code class="docutils literal notranslate"><span class="pre">__init__</span></code>
<a class="reference internal" href="#term-parameter"><span class="xref std std-term">parameter</span></a> of the same name. Because of this equivalence, these
are documented under an estimator’s <em>Parameters</em> documentation.</p>
</dd>
<dt id="term-backwards-compatibility">backwards compatibility<a class="headerlink" href="#term-backwards-compatibility" title="Link to this term">#</a></dt><dd><p>We generally try to maintain backward compatibility (i.e. interfaces
and behaviors may be extended but not changed or removed) from release
to release but this comes with some exceptions:</p>
<dl class="simple">
<dt>Public API only</dt><dd><p>The behavior of objects accessed through private identifiers
(those beginning <code class="docutils literal notranslate"><span class="pre">_</span></code>) may be changed arbitrarily between
versions.</p>
</dd>
<dt>As documented</dt><dd><p>We will generally assume that the users have adhered to the
documented parameter types and ranges. If the documentation asks
for a list and the user gives a tuple, we do not assure consistent
behavior from version to version.</p>
</dd>
<dt>Deprecation</dt><dd><p>Behaviors may change following a <a class="reference internal" href="#term-deprecation"><span class="xref std std-term">deprecation</span></a> period
(usually two releases long). Warnings are issued using Python’s
<a class="reference external" href="https://docs.python.org/3/library/warnings.html#module-warnings" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">warnings</span></code></a> module.</p>
</dd>
<dt>Keyword arguments</dt><dd><p>We may sometimes assume that all optional parameters (other than X
and y to <a class="reference internal" href="#term-fit"><span class="xref std std-term">fit</span></a> and similar methods) are passed as keyword
arguments only and may be positionally reordered.</p>
</dd>
<dt>Bug fixes and enhancements</dt><dd><p>Bug fixes and – less often – enhancements may change the behavior
of estimators, including the predictions of an estimator trained on
the same data and <a class="reference internal" href="#term-random_state"><span class="xref std std-term">random_state</span></a>. When this happens, we
attempt to note it clearly in the changelog.</p>
</dd>
<dt>Serialization</dt><dd><p>We make no assurances that pickling an estimator in one version
will allow it to be unpickled to an equivalent model in the
subsequent version. (For estimators in the sklearn package, we
issue a warning when this unpickling is attempted, even if it may
happen to work.) See <a class="reference internal" href="model_persistence.html#persistence-limitations"><span class="std std-ref">Security & Maintainability Limitations</span></a>.</p>
</dd>
<dt><a class="reference internal" href="modules/generated/sklearn.utils.estimator_checks.check_estimator.html#sklearn.utils.estimator_checks.check_estimator" title="sklearn.utils.estimator_checks.check_estimator"><code class="xref py py-func docutils literal notranslate"><span class="pre">utils.estimator_checks.check_estimator</span></code></a></dt><dd><p>We provide limited backwards compatibility assurances for the
estimator checks: we may add extra requirements on estimators
tested with this function, usually when these were informally
assumed but not formally tested.</p>
</dd>
</dl>
<p>Despite this informal contract with our users, the software is provided
as is, as stated in the license. When a release inadvertently
introduces changes that are not backward compatible, these are known
as software regressions.</p>
</dd>
<dt id="term-callable">callable<a class="headerlink" href="#term-callable" title="Link to this term">#</a></dt><dd><p>A function, class or an object which implements the <code class="docutils literal notranslate"><span class="pre">__call__</span></code>
method; anything that returns True when the argument of <a class="reference external" href="https://docs.python.org/3/library/functions.html#callable">callable()</a>.</p>
</dd>
<dt id="term-categorical-feature">categorical feature<a class="headerlink" href="#term-categorical-feature" title="Link to this term">#</a></dt><dd><p>A categorical or nominal <a class="reference internal" href="#term-feature"><span class="xref std std-term">feature</span></a> is one that has a
finite set of discrete values across the population of data.
These are commonly represented as columns of integers or
strings. Strings will be rejected by most scikit-learn
estimators, and integers will be treated as ordinal or
count-valued. For the use with most estimators, categorical
variables should be one-hot encoded. Notable exceptions include
tree-based models such as random forests and gradient boosting
models that often work better and faster with integer-coded
categorical variables.
<a class="reference internal" href="modules/generated/sklearn.preprocessing.OrdinalEncoder.html#sklearn.preprocessing.OrdinalEncoder" title="sklearn.preprocessing.OrdinalEncoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">OrdinalEncoder</span></code></a> helps encoding
string-valued categorical features as ordinal integers, and
<a class="reference internal" href="modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder" title="sklearn.preprocessing.OneHotEncoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">OneHotEncoder</span></code></a> can be used to
one-hot encode categorical features.
See also <a class="reference internal" href="modules/preprocessing.html#preprocessing-categorical-features"><span class="std std-ref">Encoding categorical features</span></a> and the
<a class="reference external" href="https://github.com/scikit-learn-contrib/category_encoders">categorical-encoding</a>
package for tools related to encoding categorical features.</p>
</dd>
<dt id="term-clone">clone<a class="headerlink" href="#term-clone" title="Link to this term">#</a></dt><dt id="term-cloned">cloned<a class="headerlink" href="#term-cloned" title="Link to this term">#</a></dt><dd><p>To copy an <a class="reference internal" href="#term-estimator-instance"><span class="xref std std-term">estimator instance</span></a> and create a new one with
identical <a class="reference internal" href="#term-parameters"><span class="xref std std-term">parameters</span></a>, but without any fitted
<a class="reference internal" href="#term-attributes"><span class="xref std std-term">attributes</span></a>, using <a class="reference internal" href="modules/generated/sklearn.base.clone.html#sklearn.base.clone" title="sklearn.base.clone"><code class="xref py py-func docutils literal notranslate"><span class="pre">clone</span></code></a>.</p>
<p>When <code class="docutils literal notranslate"><span class="pre">fit</span></code> is called, a <a class="reference internal" href="#term-meta-estimator"><span class="xref std std-term">meta-estimator</span></a> usually clones
a wrapped estimator instance before fitting the cloned instance.
(Exceptions, for legacy reasons, include
<a class="reference internal" href="modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a> and
<a class="reference internal" href="modules/generated/sklearn.pipeline.FeatureUnion.html#sklearn.pipeline.FeatureUnion" title="sklearn.pipeline.FeatureUnion"><code class="xref py py-class docutils literal notranslate"><span class="pre">FeatureUnion</span></code></a>.)</p>
<p>If the estimator’s <code class="docutils literal notranslate"><span class="pre">random_state</span></code> parameter is an integer (or if the
estimator doesn’t have a <code class="docutils literal notranslate"><span class="pre">random_state</span></code> parameter), an <em>exact clone</em>
is returned: the clone and the original estimator will give the exact
same results. Otherwise, <em>statistical clone</em> is returned: the clone
might yield different results from the original estimator. More
details can be found in <a class="reference internal" href="common_pitfalls.html#randomness"><span class="std std-ref">Controlling randomness</span></a>.</p>
</dd>
<dt id="term-common-tests">common tests<a class="headerlink" href="#term-common-tests" title="Link to this term">#</a></dt><dd><p>This refers to the tests run on almost every estimator class in
Scikit-learn to check they comply with basic API conventions. They are
available for external use through
<a class="reference internal" href="modules/generated/sklearn.utils.estimator_checks.check_estimator.html#sklearn.utils.estimator_checks.check_estimator" title="sklearn.utils.estimator_checks.check_estimator"><code class="xref py py-func docutils literal notranslate"><span class="pre">utils.estimator_checks.check_estimator</span></code></a> or
<a class="reference internal" href="modules/generated/sklearn.utils.estimator_checks.parametrize_with_checks.html#sklearn.utils.estimator_checks.parametrize_with_checks" title="sklearn.utils.estimator_checks.parametrize_with_checks"><code class="xref py py-func docutils literal notranslate"><span class="pre">utils.estimator_checks.parametrize_with_checks</span></code></a>, with most of the
implementation in <code class="docutils literal notranslate"><span class="pre">sklearn/utils/estimator_checks.py</span></code>.</p>
<p>Note: Some exceptions to the common testing regime are currently
hard-coded into the library, but we hope to replace this by marking
exceptional behaviours on the estimator using semantic <a class="reference internal" href="#term-estimator-tags"><span class="xref std std-term">estimator
tags</span></a>.</p>
</dd>
<dt id="term-cross-fitting">cross-fitting<a class="headerlink" href="#term-cross-fitting" title="Link to this term">#</a></dt><dt id="term-0">cross fitting<a class="headerlink" href="#term-0" title="Link to this term">#</a></dt><dd><p>A resampling method that iteratively partitions data into mutually
exclusive subsets to fit two stages. During the first stage, the
mutually exclusive subsets enable predictions or transformations to be
computed on data not seen during training. The computed data is then
used in the second stage. The objective is to avoid having any
overfitting in the first stage introduce bias into the input data
distribution of the second stage.
For examples of its use, see: <a class="reference internal" href="modules/generated/sklearn.preprocessing.TargetEncoder.html#sklearn.preprocessing.TargetEncoder" title="sklearn.preprocessing.TargetEncoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">TargetEncoder</span></code></a>,
<a class="reference internal" href="modules/generated/sklearn.ensemble.StackingClassifier.html#sklearn.ensemble.StackingClassifier" title="sklearn.ensemble.StackingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">StackingClassifier</span></code></a>,
<a class="reference internal" href="modules/generated/sklearn.ensemble.StackingRegressor.html#sklearn.ensemble.StackingRegressor" title="sklearn.ensemble.StackingRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">StackingRegressor</span></code></a> and
<a class="reference internal" href="modules/generated/sklearn.calibration.CalibratedClassifierCV.html#sklearn.calibration.CalibratedClassifierCV" title="sklearn.calibration.CalibratedClassifierCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">CalibratedClassifierCV</span></code></a>.</p>
</dd>
<dt id="term-cross-validation">cross-validation<a class="headerlink" href="#term-cross-validation" title="Link to this term">#</a></dt><dt id="term-1">cross validation<a class="headerlink" href="#term-1" title="Link to this term">#</a></dt><dd><p>A resampling method that iteratively partitions data into mutually
exclusive ‘train’ and ‘test’ subsets so model performance can be
evaluated on unseen data. This conserves data as avoids the need to hold
out a ‘validation’ dataset and accounts for variability as multiple
rounds of cross validation are generally performed.
See <a class="reference internal" href="modules/cross_validation.html#cross-validation"><span class="std std-ref">User Guide</span></a> for more details.</p>
</dd>
<dt id="term-deprecation">deprecation<a class="headerlink" href="#term-deprecation" title="Link to this term">#</a></dt><dd><p>We use deprecation to slowly violate our <a class="reference internal" href="#term-backwards-compatibility"><span class="xref std std-term">backwards
compatibility</span></a> assurances, usually to:</p>
<ul class="simple">
<li><p>change the default value of a parameter; or</p></li>
<li><p>remove a parameter, attribute, method, class, etc.</p></li>
</ul>
<p>We will ordinarily issue a warning when a deprecated element is used,
although there may be limitations to this. For instance, we will raise
a warning when someone sets a parameter that has been deprecated, but
may not when they access that parameter’s attribute on the estimator
instance.</p>
<p>See the <a class="reference internal" href="developers/contributing.html#contributing-deprecation"><span class="std std-ref">Contributors’ Guide</span></a>.</p>
</dd>
<dt id="term-dimensionality">dimensionality<a class="headerlink" href="#term-dimensionality" title="Link to this term">#</a></dt><dd><p>May be used to refer to the number of <a class="reference internal" href="#term-features"><span class="xref std std-term">features</span></a> (i.e.
<a class="reference internal" href="#term-n_features"><span class="xref std std-term">n_features</span></a>), or columns in a 2d feature matrix.
Dimensions are, however, also used to refer to the length of a NumPy
array’s shape, distinguishing a 1d array from a 2d matrix.</p>
</dd>
<dt id="term-docstring">docstring<a class="headerlink" href="#term-docstring" title="Link to this term">#</a></dt><dd><p>The embedded documentation for a module, class, function, etc., usually
in code as a string at the beginning of the object’s definition, and
accessible as the object’s <code class="docutils literal notranslate"><span class="pre">__doc__</span></code> attribute.</p>
<p>We try to adhere to <a class="reference external" href="https://www.python.org/dev/peps/pep-0257/">PEP257</a>, and follow <a class="reference external" href="https://numpydoc.readthedocs.io/en/latest/format.html">NumpyDoc
conventions</a>.</p>
</dd>
<dt id="term-double-underscore">double underscore<a class="headerlink" href="#term-double-underscore" title="Link to this term">#</a></dt><dt id="term-double-underscore-notation">double underscore notation<a class="headerlink" href="#term-double-underscore-notation" title="Link to this term">#</a></dt><dd><p>When specifying parameter names for nested estimators, <code class="docutils literal notranslate"><span class="pre">__</span></code> may be
used to separate between parent and child in some contexts. The most
common use is when setting parameters through a meta-estimator with
<a class="reference internal" href="#term-set_params"><span class="xref std std-term">set_params</span></a> and hence in specifying a search grid in
<a class="reference internal" href="modules/grid_search.html#grid-search"><span class="std std-ref">parameter search</span></a>. See <a class="reference internal" href="#term-parameter"><span class="xref std std-term">parameter</span></a>.
It is also used in <a class="reference internal" href="modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline.fit" title="sklearn.pipeline.Pipeline.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">pipeline.Pipeline.fit</span></code></a> for passing
<a class="reference internal" href="#term-sample-properties"><span class="xref std std-term">sample properties</span></a> to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> methods of estimators in
the pipeline.</p>
</dd>
<dt id="term-dtype">dtype<a class="headerlink" href="#term-dtype" title="Link to this term">#</a></dt><dt id="term-data-type">data type<a class="headerlink" href="#term-data-type" title="Link to this term">#</a></dt><dd><p>NumPy arrays assume a homogeneous data type throughout, available in
the <code class="docutils literal notranslate"><span class="pre">.dtype</span></code> attribute of an array (or sparse matrix). We generally
assume simple data types for scikit-learn data: float or integer.
We may support object or string data types for arrays before encoding
or vectorizing. Our estimators do not work with struct arrays, for
instance.</p>
<p>Our documentation can sometimes give information about the dtype
precision, e.g. <code class="docutils literal notranslate"><span class="pre">np.int32</span></code>, <code class="docutils literal notranslate"><span class="pre">np.int64</span></code>, etc. When the precision is
provided, it refers to the NumPy dtype. If an arbitrary precision is
used, the documentation will refer to dtype <code class="docutils literal notranslate"><span class="pre">integer</span></code> or <code class="docutils literal notranslate"><span class="pre">floating</span></code>.
Note that in this case, the precision can be platform dependent.
The <code class="docutils literal notranslate"><span class="pre">numeric</span></code> dtype refers to accepting both <code class="docutils literal notranslate"><span class="pre">integer</span></code> and <code class="docutils literal notranslate"><span class="pre">floating</span></code>.</p>
<p>When it comes to choosing between 64-bit dtype (i.e. <code class="docutils literal notranslate"><span class="pre">np.float64</span></code> and
<code class="docutils literal notranslate"><span class="pre">np.int64</span></code>) and 32-bit dtype (i.e. <code class="docutils literal notranslate"><span class="pre">np.float32</span></code> and <code class="docutils literal notranslate"><span class="pre">np.int32</span></code>), it
boils down to a trade-off between efficiency and precision. The 64-bit
types offer more accurate results due to their lower floating-point
error, but demand more computational resources, resulting in slower
operations and increased memory usage. In contrast, 32-bit types
promise enhanced operation speed and reduced memory consumption, but
introduce a larger floating-point error. The efficiency improvement are
dependent on lower level optimization such as like vectorization,
single instruction multiple dispatch (SIMD), or cache optimization but
crucially on the compatibility of the algorithm in use.</p>
<p>Specifically, the choice of precision should account for whether the
employed algorithm can effectively leverage <code class="docutils literal notranslate"><span class="pre">np.float32</span></code>. Some
algorithms, especially certain minimization methods, are exclusively
coded for <code class="docutils literal notranslate"><span class="pre">np.float64</span></code>, meaning that even if <code class="docutils literal notranslate"><span class="pre">np.float32</span></code> is passed, it
triggers an automatic conversion back to <code class="docutils literal notranslate"><span class="pre">np.float64</span></code>. This not only
negates the intended computational savings but also introduces
additional overhead, making operations with <code class="docutils literal notranslate"><span class="pre">np.float32</span></code> unexpectedly
slower and more memory-intensive due to this extra conversion step.</p>
</dd>
<dt id="term-duck-typing">duck typing<a class="headerlink" href="#term-duck-typing" title="Link to this term">#</a></dt><dd><p>We try to apply <a class="reference external" href="https://en.wikipedia.org/wiki/Duck_typing">duck typing</a> to determine how to
handle some input values (e.g. checking whether a given estimator is
a classifier). That is, we avoid using <code class="docutils literal notranslate"><span class="pre">isinstance</span></code> where possible,
and rely on the presence or absence of attributes to determine an
object’s behaviour. Some nuance is required when following this
approach:</p>
<ul>
<li><p>For some estimators, an attribute may only be available once it is
<a class="reference internal" href="#term-fitted"><span class="xref std std-term">fitted</span></a>. For instance, we cannot a priori determine if
<a class="reference internal" href="#term-predict_proba"><span class="xref std std-term">predict_proba</span></a> is available in a grid search where the grid
includes alternating between a probabilistic and a non-probabilistic
predictor in the final step of the pipeline. In the following, we
can only determine if <code class="docutils literal notranslate"><span class="pre">clf</span></code> is probabilistic after fitting it on
some data:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchCV</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">SGDClassifier</span>
<span class="gp">>>> </span><span class="n">clf</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">SGDClassifier</span><span class="p">(),</span>
<span class="gp">... </span> <span class="n">param_grid</span><span class="o">=</span><span class="p">{</span><span class="s1">'loss'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'log_loss'</span><span class="p">,</span> <span class="s1">'hinge'</span><span class="p">]})</span>
</pre></div>
</div>
<p>This means that we can only check for duck-typed attributes after
fitting, and that we must be careful to make <a class="reference internal" href="#term-meta-estimators"><span class="xref std std-term">meta-estimators</span></a>
only present attributes according to the state of the underlying
estimator after fitting.</p>
</li>
<li><p>Checking if an attribute is present (using <code class="docutils literal notranslate"><span class="pre">hasattr</span></code>) is in general
just as expensive as getting the attribute (<code class="docutils literal notranslate"><span class="pre">getattr</span></code> or dot
notation). In some cases, getting the attribute may indeed be
expensive (e.g. for some implementations of
<a class="reference internal" href="#term-feature_importances_"><span class="xref std std-term">feature_importances_</span></a>, which may suggest this is an API design
flaw). So code which does <code class="docutils literal notranslate"><span class="pre">hasattr</span></code> followed by <code class="docutils literal notranslate"><span class="pre">getattr</span></code> should
be avoided; <code class="docutils literal notranslate"><span class="pre">getattr</span></code> within a try-except block is preferred.</p></li>
<li><p>For determining some aspects of an estimator’s expectations or
support for some feature, we use <a class="reference internal" href="#term-estimator-tags"><span class="xref std std-term">estimator tags</span></a> instead of
duck typing.</p></li>
</ul>
</dd>
<dt id="term-early-stopping">early stopping<a class="headerlink" href="#term-early-stopping" title="Link to this term">#</a></dt><dd><p>This consists in stopping an iterative optimization method before the
convergence of the training loss, to avoid over-fitting. This is
generally done by monitoring the generalization score on a validation
set. When available, it is activated through the parameter
<code class="docutils literal notranslate"><span class="pre">early_stopping</span></code> or by setting a positive <a class="reference internal" href="#term-n_iter_no_change"><span class="xref std std-term">n_iter_no_change</span></a>.</p>
</dd>
<dt id="term-estimator-instance">estimator instance<a class="headerlink" href="#term-estimator-instance" title="Link to this term">#</a></dt><dd><p>We sometimes use this terminology to distinguish an <a class="reference internal" href="#term-estimator"><span class="xref std std-term">estimator</span></a>
class from a constructed instance. For example, in the following,
<code class="docutils literal notranslate"><span class="pre">cls</span></code> is an estimator class, while <code class="docutils literal notranslate"><span class="pre">est1</span></code> and <code class="docutils literal notranslate"><span class="pre">est2</span></code> are
instances:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="bp">cls</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span>
<span class="n">est1</span> <span class="o">=</span> <span class="bp">cls</span><span class="p">()</span>
<span class="n">est2</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">()</span>
</pre></div>
</div>
</dd>
<dt id="term-examples">examples<a class="headerlink" href="#term-examples" title="Link to this term">#</a></dt><dd><p>We try to give examples of basic usage for most functions and
classes in the API:</p>
<ul class="simple">
<li><p>as doctests in their docstrings (i.e. within the <code class="docutils literal notranslate"><span class="pre">sklearn/</span></code> library
code itself).</p></li>
<li><p>as examples in the <a class="reference internal" href="auto_examples/index.html#general-examples"><span class="std std-ref">example gallery</span></a>
rendered (using <a class="reference external" href="https://sphinx-gallery.readthedocs.io/">sphinx-gallery</a>) from scripts in the
<code class="docutils literal notranslate"><span class="pre">examples/</span></code> directory, exemplifying key features or parameters
of the estimator/function. These should also be referenced from the
User Guide.</p></li>
<li><p>sometimes in the <a class="reference internal" href="user_guide.html#user-guide"><span class="std std-ref">User Guide</span></a> (built from <code class="docutils literal notranslate"><span class="pre">doc/</span></code>)
alongside a technical description of the estimator.</p></li>
</ul>
</dd>
<dt id="term-experimental">experimental<a class="headerlink" href="#term-experimental" title="Link to this term">#</a></dt><dd><p>An experimental tool is already usable but its public API, such as
default parameter values or fitted attributes, is still subject to
change in future versions without the usual <a class="reference internal" href="#term-deprecation"><span class="xref std std-term">deprecation</span></a>
warning policy.</p>
</dd>
<dt id="term-evaluation-metric">evaluation metric<a class="headerlink" href="#term-evaluation-metric" title="Link to this term">#</a></dt><dt id="term-evaluation-metrics">evaluation metrics<a class="headerlink" href="#term-evaluation-metrics" title="Link to this term">#</a></dt><dd><p>Evaluation metrics give a measure of how well a model performs. We may
use this term specifically to refer to the functions in <a class="reference internal" href="api/sklearn.metrics.html#module-sklearn.metrics" title="sklearn.metrics"><code class="xref py py-mod docutils literal notranslate"><span class="pre">metrics</span></code></a>
(disregarding <a class="reference internal" href="api/sklearn.metrics.html#module-sklearn.metrics.pairwise" title="sklearn.metrics.pairwise"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pairwise</span></code></a>), as distinct from the
<a class="reference internal" href="#term-score"><span class="xref std std-term">score</span></a> method and the <a class="reference internal" href="#term-scoring"><span class="xref std std-term">scoring</span></a> API used in cross
validation. See <a class="reference internal" href="modules/model_evaluation.html#model-evaluation"><span class="std std-ref">Metrics and scoring: quantifying the quality of predictions</span></a>.</p>
<p>These functions usually accept a ground truth (or the raw data
where the metric evaluates clustering without a ground truth) and a
prediction, be it the output of <a class="reference internal" href="#term-predict"><span class="xref std std-term">predict</span></a> (<code class="docutils literal notranslate"><span class="pre">y_pred</span></code>),
of <a class="reference internal" href="#term-predict_proba"><span class="xref std std-term">predict_proba</span></a> (<code class="docutils literal notranslate"><span class="pre">y_proba</span></code>), or of an arbitrary score
function including <a class="reference internal" href="#term-decision_function"><span class="xref std std-term">decision_function</span></a> (<code class="docutils literal notranslate"><span class="pre">y_score</span></code>).
Functions are usually named to end with <code class="docutils literal notranslate"><span class="pre">_score</span></code> if a greater
score indicates a better model, and <code class="docutils literal notranslate"><span class="pre">_loss</span></code> if a lesser score
indicates a better model. This diversity of interface motivates
the scoring API.</p>
<p>Note that some estimators can calculate metrics that are not included
in <a class="reference internal" href="api/sklearn.metrics.html#module-sklearn.metrics" title="sklearn.metrics"><code class="xref py py-mod docutils literal notranslate"><span class="pre">metrics</span></code></a> and are estimator-specific, notably model
likelihoods.</p>
</dd>
<dt id="term-estimator-tags">estimator tags<a class="headerlink" href="#term-estimator-tags" title="Link to this term">#</a></dt><dd><p>Estimator tags describe certain capabilities of an estimator. This would
enable some runtime behaviors based on estimator inspection, but it
also allows each estimator to be tested for appropriate invariances
while being excepted from other <a class="reference internal" href="#term-common-tests"><span class="xref std std-term">common tests</span></a>.</p>
<p>Some aspects of estimator tags are currently determined through
the <a class="reference internal" href="#term-duck-typing"><span class="xref std std-term">duck typing</span></a> of methods like <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code> and through
some special attributes on estimator objects:</p>
<p>For more detailed info, see <a class="reference internal" href="developers/develop.html#estimator-tags"><span class="std std-ref">Estimator Tags</span></a>.</p>
</dd>
<dt id="term-feature">feature<a class="headerlink" href="#term-feature" title="Link to this term">#</a></dt><dt id="term-features">features<a class="headerlink" href="#term-features" title="Link to this term">#</a></dt><dt id="term-feature-vector">feature vector<a class="headerlink" href="#term-feature-vector" title="Link to this term">#</a></dt><dd><p>In the abstract, a feature is a function (in its mathematical sense)
mapping a sampled object to a numeric or categorical quantity.
“Feature” is also commonly used to refer to these quantities, being the
individual elements of a vector representing a sample. In a data
matrix, features are represented as columns: each column contains the
result of applying a feature function to a set of samples.</p>
<p>Elsewhere features are known as attributes, predictors, regressors, or
independent variables.</p>
<p>Nearly all estimators in scikit-learn assume that features are numeric,
finite and not missing, even when they have semantically distinct
domains and distributions (categorical, ordinal, count-valued,
real-valued, interval). See also <a class="reference internal" href="#term-categorical-feature"><span class="xref std std-term">categorical feature</span></a> and
<a class="reference internal" href="#term-missing-values"><span class="xref std std-term">missing values</span></a>.</p>
<p><code class="docutils literal notranslate"><span class="pre">n_features</span></code> indicates the number of features in a dataset.</p>
</dd>
<dt id="term-fitting">fitting<a class="headerlink" href="#term-fitting" title="Link to this term">#</a></dt><dd><p>Calling <a class="reference internal" href="#term-fit"><span class="xref std std-term">fit</span></a> (or <a class="reference internal" href="#term-fit_transform"><span class="xref std std-term">fit_transform</span></a>, <a class="reference internal" href="#term-fit_predict"><span class="xref std std-term">fit_predict</span></a>,
etc.) on an estimator.</p>
</dd>
<dt id="term-fitted">fitted<a class="headerlink" href="#term-fitted" title="Link to this term">#</a></dt><dd><p>The state of an estimator after <a class="reference internal" href="#term-fitting"><span class="xref std std-term">fitting</span></a>.</p>
<p>There is no conventional procedure for checking if an estimator
is fitted. However, an estimator that is not fitted:</p>
<ul class="simple">
<li><p>should raise <a class="reference internal" href="modules/generated/sklearn.exceptions.NotFittedError.html#sklearn.exceptions.NotFittedError" title="sklearn.exceptions.NotFittedError"><code class="xref py py-class docutils literal notranslate"><span class="pre">exceptions.NotFittedError</span></code></a> when a prediction
method (<a class="reference internal" href="#term-predict"><span class="xref std std-term">predict</span></a>, <a class="reference internal" href="#term-transform"><span class="xref std std-term">transform</span></a>, etc.) is called.
(<a class="reference internal" href="modules/generated/sklearn.utils.validation.check_is_fitted.html#sklearn.utils.validation.check_is_fitted" title="sklearn.utils.validation.check_is_fitted"><code class="xref py py-func docutils literal notranslate"><span class="pre">utils.validation.check_is_fitted</span></code></a> is used internally
for this purpose.)</p></li>
<li><p>should not have any <a class="reference internal" href="#term-attributes"><span class="xref std std-term">attributes</span></a> beginning with an alphabetic
character and ending with an underscore. (Note that a descriptor for
the attribute may still be present on the class, but hasattr should
return False)</p></li>
</ul>
</dd>
<dt id="term-function">function<a class="headerlink" href="#term-function" title="Link to this term">#</a></dt><dd><p>We provide ad hoc function interfaces for many algorithms, while
<a class="reference internal" href="#term-estimator"><span class="xref std std-term">estimator</span></a> classes provide a more consistent interface.</p>
<p>In particular, Scikit-learn may provide a function interface that fits
a model to some data and returns the learnt model parameters, as in
<a class="reference internal" href="modules/generated/sklearn.linear_model.enet_path.html#sklearn.linear_model.enet_path" title="sklearn.linear_model.enet_path"><code class="xref py py-func docutils literal notranslate"><span class="pre">linear_model.enet_path</span></code></a>. For transductive models, this also
returns the embedding or cluster labels, as in
<a class="reference internal" href="modules/generated/sklearn.manifold.spectral_embedding.html#sklearn.manifold.spectral_embedding" title="sklearn.manifold.spectral_embedding"><code class="xref py py-func docutils literal notranslate"><span class="pre">manifold.spectral_embedding</span></code></a> or <a class="reference internal" href="modules/generated/dbscan-function.html#sklearn.cluster.dbscan" title="sklearn.cluster.dbscan"><code class="xref py py-func docutils literal notranslate"><span class="pre">cluster.dbscan</span></code></a>. Many
preprocessing transformers also provide a function interface, akin to
calling <a class="reference internal" href="#term-fit_transform"><span class="xref std std-term">fit_transform</span></a>, as in
<a class="reference internal" href="modules/generated/sklearn.preprocessing.maxabs_scale.html#sklearn.preprocessing.maxabs_scale" title="sklearn.preprocessing.maxabs_scale"><code class="xref py py-func docutils literal notranslate"><span class="pre">preprocessing.maxabs_scale</span></code></a>. Users should be careful to avoid
<a class="reference internal" href="#term-data-leakage"><span class="xref std std-term">data leakage</span></a> when making use of these
<code class="docutils literal notranslate"><span class="pre">fit_transform</span></code>-equivalent functions.</p>
<p>We do not have a strict policy about when to or when not to provide
function forms of estimators, but maintainers should consider
consistency with existing interfaces, and whether providing a function
would lead users astray from best practices (as regards data leakage,
etc.)</p>