.. currentmodule:: sklearn
.. _glossary:
=========================================
Glossary of Common Terms and API Elements
=========================================
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.
We begin by listing general concepts (and any that didn't fit elsewhere), but
more specific sets of related terms are listed below:
:ref:`glossary_estimator_types`, :ref:`glossary_target_types`,
:ref:`glossary_methods`, :ref:`glossary_parameters`,
:ref:`glossary_attributes`, :ref:`glossary_sample_props`.
General Concepts
================
.. glossary::
1d
1d array
One-dimensional array. A NumPy array whose ``.shape`` has length 1.
A vector.
2d
2d array
Two-dimensional array. A NumPy array whose ``.shape`` has length 2.
Often represents a matrix.
API
Refers to both the *specific* interfaces for estimators implemented in
Scikit-learn and the *generalized* conventions across types of
estimators as described in this glossary and :ref:`overviewed in the
contributor documentation `.
The specific interfaces that constitute Scikit-learn's public API are
largely documented in :ref:`api_ref`. However, we less formally consider
anything as public API if none of the identifiers required to access it
begins with ``_``. We generally try to maintain :term:`backwards
compatibility` for all objects in the public API.
Private API, including functions, modules and methods beginning ``_``
are not assured to be stable.
array-like
The most common data format for *input* to Scikit-learn estimators and
functions, array-like is any type object for which
:func:`numpy.asarray` will produce an array of appropriate shape
(usually 1 or 2-dimensional) of appropriate dtype (usually numeric).
This includes:
* a numpy array
* a list of numbers
* a list of length-k lists of numbers for some fixed length k
* a :class:`pandas.DataFrame` with all columns numeric
* a numeric :class:`pandas.Series`
It excludes:
* a :term:`sparse matrix`
* a sparse array
* an iterator
* a generator
Note that *output* from scikit-learn estimators and functions (e.g.
predictions) should generally be arrays or sparse matrices, or lists
thereof (as in multi-output :class:`tree.DecisionTreeClassifier`'s
``predict_proba``). An estimator where ``predict()`` returns a list or
a `pandas.Series` is not valid.
attribute
attributes
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 :term:`fit` or
:term:`partial_fit`. These are what is documented under an estimator's
*Attributes* documentation. The information stored in attributes is
usually either: sufficient statistics used for prediction or
transformation; :term:`transductive` outputs such as :term:`labels_` or
:term:`embedding_`; or diagnostic data, such as
:term:`feature_importances_`.
Common attributes are listed :ref:`below `.
A public attribute may have the same name as a constructor
:term:`parameter`, with a ``_`` appended. This is used to store a
validated or estimated version of the user's input. For example,
:class:`decomposition.PCA` is constructed with an ``n_components``
parameter. From this, together with other parameters and the data,
PCA estimates the attribute ``n_components_``.
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.
A public attribute on an estimator instance that does not end in an
underscore should be the stored, unmodified value of an ``__init__``
:term:`parameter` of the same name. Because of this equivalence, these
are documented under an estimator's *Parameters* documentation.
backwards compatibility
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:
Public API only
The behavior of objects accessed through private identifiers
(those beginning ``_``) may be changed arbitrarily between
versions.
As documented
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.
Deprecation
Behaviors may change following a :term:`deprecation` period
(usually two releases long). Warnings are issued using Python's
:mod:`warnings` module.
Keyword arguments
We may sometimes assume that all optional parameters (other than X
and y to :term:`fit` and similar methods) are passed as keyword
arguments only and may be positionally reordered.
Bug fixes and enhancements
Bug fixes and -- less often -- enhancements may change the behavior
of estimators, including the predictions of an estimator trained on
the same data and :term:`random_state`. When this happens, we
attempt to note it clearly in the changelog.
Serialization
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 :ref:`persistence_limitations`.
:func:`utils.estimator_checks.check_estimator`
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.
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.
callable
A function, class or an object which implements the ``__call__``
method; anything that returns True when the argument of `callable()
`_.
categorical feature
A categorical or nominal :term:`feature` 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.
:class:`~sklearn.preprocessing.OrdinalEncoder` helps encoding
string-valued categorical features as ordinal integers, and
:class:`~sklearn.preprocessing.OneHotEncoder` can be used to
one-hot encode categorical features.
See also :ref:`preprocessing_categorical_features` and the
`categorical-encoding
`_
package for tools related to encoding categorical features.
clone
cloned
To copy an :term:`estimator instance` and create a new one with
identical :term:`parameters`, but without any fitted
:term:`attributes`, using :func:`~sklearn.base.clone`.
When ``fit`` is called, a :term:`meta-estimator` usually clones
a wrapped estimator instance before fitting the cloned instance.
(Exceptions, for legacy reasons, include
:class:`~pipeline.Pipeline` and
:class:`~pipeline.FeatureUnion`.)
If the estimator's `random_state` parameter is an integer (or if the
estimator doesn't have a `random_state` parameter), an *exact clone*
is returned: the clone and the original estimator will give the exact
same results. Otherwise, *statistical clone* is returned: the clone
might yield different results from the original estimator. More
details can be found in :ref:`randomness`.
common tests
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
:func:`utils.estimator_checks.check_estimator`, with most of the
implementation in ``sklearn/utils/estimator_checks.py``.
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 :term:`estimator
tags`.
cross-fitting
cross fitting
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: :class:`~preprocessing.TargetEncoder`,
:class:`~ensemble.StackingClassifier`,
:class:`~ensemble.StackingRegressor` and
:class:`~calibration.CalibratedClassifierCV`.
cross-validation
cross validation
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 :ref:`User Guide ` for more details.
deprecation
We use deprecation to slowly violate our :term:`backwards
compatibility` assurances, usually to:
* change the default value of a parameter; or
* remove a parameter, attribute, method, class, etc.
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.
See the :ref:`Contributors' Guide `.
dimensionality
May be used to refer to the number of :term:`features` (i.e.
:term:`n_features`), 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.
docstring
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 ``__doc__`` attribute.
We try to adhere to `PEP257
`_, and follow `NumpyDoc
conventions `_.
double underscore
double underscore notation
When specifying parameter names for nested estimators, ``__`` 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
:term:`set_params` and hence in specifying a search grid in
:ref:`parameter search `. See :term:`parameter`.
It is also used in :meth:`pipeline.Pipeline.fit` for passing
:term:`sample properties` to the ``fit`` methods of estimators in
the pipeline.
dtype
data type
NumPy arrays assume a homogeneous data type throughout, available in
the ``.dtype`` 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.
Our documentation can sometimes give information about the dtype
precision, e.g. `np.int32`, `np.int64`, etc. When the precision is
provided, it refers to the NumPy dtype. If an arbitrary precision is
used, the documentation will refer to dtype `integer` or `floating`.
Note that in this case, the precision can be platform dependent.
The `numeric` dtype refers to accepting both `integer` and `floating`.
When it comes to choosing between 64-bit dtype (i.e. `np.float64` and
`np.int64`) and 32-bit dtype (i.e. `np.float32` and `np.int32`), 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.
Specifically, the choice of precision should account for whether the
employed algorithm can effectively leverage `np.float32`. Some
algorithms, especially certain minimization methods, are exclusively
coded for `np.float64`, meaning that even if `np.float32` is passed, it
triggers an automatic conversion back to `np.float64`. This not only
negates the intended computational savings but also introduces
additional overhead, making operations with `np.float32` unexpectedly
slower and more memory-intensive due to this extra conversion step.
duck typing
We try to apply `duck typing
`_ to determine how to
handle some input values (e.g. checking whether a given estimator is
a classifier). That is, we avoid using ``isinstance`` 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:
* For some estimators, an attribute may only be available once it is
:term:`fitted`. For instance, we cannot a priori determine if
:term:`predict_proba` 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 ``clf`` is probabilistic after fitting it on
some data::
>>> from sklearn.model_selection import GridSearchCV
>>> from sklearn.linear_model import SGDClassifier
>>> clf = GridSearchCV(SGDClassifier(),
... param_grid={'loss': ['log_loss', 'hinge']})
This means that we can only check for duck-typed attributes after
fitting, and that we must be careful to make :term:`meta-estimators`
only present attributes according to the state of the underlying
estimator after fitting.
* Checking if an attribute is present (using ``hasattr``) is in general
just as expensive as getting the attribute (``getattr`` or dot
notation). In some cases, getting the attribute may indeed be
expensive (e.g. for some implementations of
:term:`feature_importances_`, which may suggest this is an API design
flaw). So code which does ``hasattr`` followed by ``getattr`` should
be avoided; ``getattr`` within a try-except block is preferred.
* For determining some aspects of an estimator's expectations or
support for some feature, we use :term:`estimator tags` instead of
duck typing.
early stopping
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
``early_stopping`` or by setting a positive :term:`n_iter_no_change`.
estimator instance
We sometimes use this terminology to distinguish an :term:`estimator`
class from a constructed instance. For example, in the following,
``cls`` is an estimator class, while ``est1`` and ``est2`` are
instances::
cls = RandomForestClassifier
est1 = cls()
est2 = RandomForestClassifier()
examples
We try to give examples of basic usage for most functions and
classes in the API:
* as doctests in their docstrings (i.e. within the ``sklearn/`` library
code itself).
* as examples in the :ref:`example gallery `
rendered (using `sphinx-gallery
`_) from scripts in the
``examples/`` directory, exemplifying key features or parameters
of the estimator/function. These should also be referenced from the
User Guide.
* sometimes in the :ref:`User Guide ` (built from ``doc/``)
alongside a technical description of the estimator.
experimental
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 :term:`deprecation`
warning policy.
evaluation metric
evaluation metrics
Evaluation metrics give a measure of how well a model performs. We may
use this term specifically to refer to the functions in :mod:`~sklearn.metrics`
(disregarding :mod:`~sklearn.metrics.pairwise`), as distinct from the
:term:`score` method and the :term:`scoring` API used in cross
validation. See :ref:`model_evaluation`.
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 :term:`predict` (``y_pred``),
of :term:`predict_proba` (``y_proba``), or of an arbitrary score
function including :term:`decision_function` (``y_score``).
Functions are usually named to end with ``_score`` if a greater
score indicates a better model, and ``_loss`` if a lesser score
indicates a better model. This diversity of interface motivates
the scoring API.
Note that some estimators can calculate metrics that are not included
in :mod:`~sklearn.metrics` and are estimator-specific, notably model
likelihoods.
estimator tags
A proposed feature (e.g. :issue:`8022`) by which the capabilities of an
estimator are described through a set of semantic tags. 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 :term:`common tests`.
Some aspects of estimator tags are currently determined through
the :term:`duck typing` of methods like ``predict_proba`` and through
some special attributes on estimator objects:
.. glossary::
``_estimator_type``
This string-valued attribute identifies an estimator as being a
classifier, regressor, etc. It is set by mixins such as
:class:`base.ClassifierMixin`, but needs to be more explicitly
adopted on a :term:`meta-estimator`. Its value should usually be
checked by way of a helper such as :func:`base.is_classifier`.
For more detailed info, see :ref:`estimator_tags`.
feature
features
feature vector
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.
Elsewhere features are known as attributes, predictors, regressors, or
independent variables.
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 :term:`categorical feature` and
:term:`missing values`.
``n_features`` indicates the number of features in a dataset.
fitting
Calling :term:`fit` (or :term:`fit_transform`, :term:`fit_predict`,
etc.) on an estimator.
fitted
The state of an estimator after :term:`fitting`.
There is no conventional procedure for checking if an estimator
is fitted. However, an estimator that is not fitted:
* should raise :class:`exceptions.NotFittedError` when a prediction
method (:term:`predict`, :term:`transform`, etc.) is called.
(:func:`utils.validation.check_is_fitted` is used internally
for this purpose.)
* should not have any :term:`attributes` 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)
function
We provide ad hoc function interfaces for many algorithms, while
:term:`estimator` classes provide a more consistent interface.
In particular, Scikit-learn may provide a function interface that fits
a model to some data and returns the learnt model parameters, as in
:func:`linear_model.enet_path`. For transductive models, this also
returns the embedding or cluster labels, as in
:func:`manifold.spectral_embedding` or :func:`cluster.dbscan`. Many
preprocessing transformers also provide a function interface, akin to
calling :term:`fit_transform`, as in
:func:`preprocessing.maxabs_scale`. Users should be careful to avoid
:term:`data leakage` when making use of these
``fit_transform``-equivalent functions.
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.)
gallery
See :term:`examples`.
hyperparameter
hyper-parameter
See :term:`parameter`.
impute
imputation
Most machine learning algorithms require that their inputs have no
:term:`missing values`, and will not work if this requirement is
violated. Algorithms that attempt to fill in (or impute) missing values
are referred to as imputation algorithms.
indexable
An :term:`array-like`, :term:`sparse matrix`, pandas DataFrame or
sequence (usually a list).
induction
inductive
Inductive (contrasted with :term:`transductive`) machine learning
builds a model of some data that can then be applied to new instances.
Most estimators in Scikit-learn are inductive, having :term:`predict`
and/or :term:`transform` methods.
joblib
A Python library (https://joblib.readthedocs.io) used in Scikit-learn to
facilite simple parallelism and caching. Joblib is oriented towards
efficiently working with numpy arrays, such as through use of
:term:`memory mapping`. See :ref:`parallelism` for more
information.
label indicator matrix
multilabel indicator matrix
multilabel indicator matrices
The format used to represent multilabel data, where each row of a 2d
array or sparse matrix corresponds to a sample, each column
corresponds to a class, and each element is 1 if the sample is labeled
with the class and 0 if not.
leakage
data leakage
A problem in cross validation where generalization performance can be
over-estimated since knowledge of the test data was inadvertently
included in training a model. This is a risk, for instance, when
applying a :term:`transformer` to the entirety of a dataset rather
than each training portion in a cross validation split.
We aim to provide interfaces (such as :mod:`~sklearn.pipeline` and
:mod:`~sklearn.model_selection`) that shield the user from data leakage.
memmapping
memory map
memory mapping
A memory efficiency strategy that keeps data on disk rather than
copying it into main memory. Memory maps can be created for arrays
that can be read, written, or both, using :obj:`numpy.memmap`. When
using :term:`joblib` to parallelize operations in Scikit-learn, it
may automatically memmap large arrays to reduce memory duplication
overhead in multiprocessing.
missing values
Most Scikit-learn estimators do not work with missing values. When they
do (e.g. in :class:`impute.SimpleImputer`), NaN is the preferred
representation of missing values in float arrays. If the array has
integer dtype, NaN cannot be represented. For this reason, we support
specifying another ``missing_values`` value when :term:`imputation` or
learning can be performed in integer space.
:term:`Unlabeled data ` is a special case of missing
values in the :term:`target`.
``n_features``
The number of :term:`features`.
``n_outputs``
The number of :term:`outputs` in the :term:`target`.
``n_samples``
The number of :term:`samples`.
``n_targets``
Synonym for :term:`n_outputs`.
narrative docs
narrative documentation
An alias for :ref:`User Guide `, i.e. documentation written
in ``doc/modules/``. Unlike the :ref:`API reference ` provided
through docstrings, the User Guide aims to:
* group tools provided by Scikit-learn together thematically or in
terms of usage;
* motivate why someone would use each particular tool, often through
comparison;
* provide both intuitive and technical descriptions of tools;
* provide or link to :term:`examples` of using key features of a
tool.
np
A shorthand for Numpy due to the conventional import statement::
import numpy as np
online learning
Where a model is iteratively updated by receiving each batch of ground
truth :term:`targets` soon after making predictions on corresponding
batch of data. Intrinsically, the model must be usable for prediction
after each batch. See :term:`partial_fit`.
out-of-core
An efficiency strategy where not all the data is stored in main memory
at once, usually by performing learning on batches of data. See
:term:`partial_fit`.
outputs
Individual scalar/categorical variables per sample in the
:term:`target`. For example, in multilabel classification each
possible label corresponds to a binary output. Also called *responses*,
*tasks* or *targets*.
See :term:`multiclass multioutput` and :term:`continuous multioutput`.
pair
A tuple of length two.
parameter
parameters
param
params
We mostly use *parameter* to refer to the aspects of an estimator that
can be specified in its construction. For example, ``max_depth`` and
``random_state`` are parameters of :class:`~ensemble.RandomForestClassifier`.
Parameters to an estimator's constructor are stored unmodified as
attributes on the estimator instance, and conventionally start with an
alphabetic character and end with an alphanumeric character. Each
estimator's constructor parameters are described in the estimator's
docstring.
We do not use parameters in the statistical sense, where parameters are
values that specify a model and can be estimated from data. What we
call parameters might be what statisticians call hyperparameters to the
model: aspects for configuring model structure that are often not
directly learnt from data. However, our parameters are also used to
prescribe modeling operations that do not affect the learnt model, such
as :term:`n_jobs` for controlling parallelism.
When talking about the parameters of a :term:`meta-estimator`, we may
also be including the parameters of the estimators wrapped by the
meta-estimator. Ordinarily, these nested parameters are denoted by
using a :term:`double underscore` (``__``) to separate between the
estimator-as-parameter and its parameter. Thus ``clf =
BaggingClassifier(estimator=DecisionTreeClassifier(max_depth=3))``
has a deep parameter ``estimator__max_depth`` with value ``3``,
which is accessible with ``clf.estimator.max_depth`` or
``clf.get_params()['estimator__max_depth']``.
The list of parameters and their current values can be retrieved from
an :term:`estimator instance` using its :term:`get_params` method.
Between construction and fitting, parameters may be modified using
:term:`set_params`. To enable this, parameters are not ordinarily
validated or altered when the estimator is constructed, or when each
parameter is set. Parameter validation is performed when :term:`fit` is
called.
Common parameters are listed :ref:`below `.
pairwise metric
pairwise metrics
In its broad sense, a pairwise metric defines a function for measuring
similarity or dissimilarity between two samples (with each ordinarily
represented as a :term:`feature vector`). We particularly provide
implementations of distance metrics (as well as improper metrics like
Cosine Distance) through :func:`metrics.pairwise_distances`, and of
kernel functions (a constrained class of similarity functions) in
:func:`metrics.pairwise.pairwise_kernels`. These can compute pairwise distance
matrices that are symmetric and hence store data redundantly.
See also :term:`precomputed` and :term:`metric`.
Note that for most distance metrics, we rely on implementations from
:mod:`scipy.spatial.distance`, but may reimplement for efficiency in
our context. The :class:`metrics.DistanceMetric` interface is used to implement
distance metrics for integration with efficient neighbors search.
pd
A shorthand for `Pandas `_ due to the
conventional import statement::
import pandas as pd
precomputed
Where algorithms rely on :term:`pairwise metrics`, and can be computed
from pairwise metrics alone, we often allow the user to specify that
the :term:`X` provided is already in the pairwise (dis)similarity
space, rather than in a feature space. That is, when passed to
:term:`fit`, it is a square, symmetric matrix, with each vector
indicating (dis)similarity to every sample, and when passed to
prediction/transformation methods, each row corresponds to a testing
sample and each column to a training sample.
Use of precomputed X is usually indicated by setting a ``metric``,
``affinity`` or ``kernel`` parameter to the string 'precomputed'. If
this is the case, then the estimator should set the `pairwise`
estimator tag as True.
rectangular
Data that can be represented as a matrix with :term:`samples` on the
first axis and a fixed, finite set of :term:`features` on the second
is called rectangular.
This term excludes samples with non-vectorial structures, such as text,
an image of arbitrary size, a time series of arbitrary length, a set of
vectors, etc. The purpose of a :term:`vectorizer` is to produce
rectangular forms of such data.
sample
samples
We usually use this term as a noun to indicate a single feature vector.
Elsewhere a sample is called an instance, data point, or observation.
``n_samples`` indicates the number of samples in a dataset, being the
number of rows in a data array :term:`X`.
sample property
sample properties
A sample property is data for each sample (e.g. an array of length
n_samples) passed to an estimator method or a similar function,
alongside but distinct from the :term:`features` (``X``) and
:term:`target` (``y``). The most prominent example is
:term:`sample_weight`; see others at :ref:`glossary_sample_props`.
As of version 0.19 we do not have a consistent approach to handling
sample properties and their routing in :term:`meta-estimators`, though
a ``fit_params`` parameter is often used.
scikit-learn-contrib
A venue for publishing Scikit-learn-compatible libraries that are
broadly authorized by the core developers and the contrib community,
but not maintained by the core developer team.
See https://scikit-learn-contrib.github.io.
scikit-learn enhancement proposals
SLEP
SLEPs
Changes to the API principles and changes to dependencies or supported
versions happen via a :ref:`SLEP ` and follows the
decision-making process outlined in :ref:`governance`.
For all votes, a proposal must have been made public and discussed before the
vote. Such a proposal must be a consolidated document, in the form of a
"Scikit-Learn Enhancement Proposal" (SLEP), rather than a long discussion on an
issue. A SLEP must be submitted as a pull-request to
`enhancement proposals `_ using the
`SLEP template `_.
semi-supervised
semi-supervised learning
semisupervised
Learning where the expected prediction (label or ground truth) is only
available for some samples provided as training data when
:term:`fitting` the model. We conventionally apply the label ``-1``
to :term:`unlabeled` samples in semi-supervised classification.
sparse matrix
sparse graph
A representation of two-dimensional numeric data that is more memory
efficient the corresponding dense numpy array where almost all elements
are zero. We use the :mod:`scipy.sparse` framework, which provides
several underlying sparse data representations, or *formats*.
Some formats are more efficient than others for particular tasks, and
when a particular format provides especial benefit, we try to document
this fact in Scikit-learn parameter descriptions.
Some sparse matrix formats (notably CSR, CSC, COO and LIL) distinguish
between *implicit* and *explicit* zeros. Explicit zeros are stored
(i.e. they consume memory in a ``data`` array) in the data structure,
while implicit zeros correspond to every element not otherwise defined
in explicit storage.
Two semantics for sparse matrices are used in Scikit-learn:
matrix semantics
The sparse matrix is interpreted as an array with implicit and
explicit zeros being interpreted as the number 0. This is the
interpretation most often adopted, e.g. when sparse matrices
are used for feature matrices or :term:`multilabel indicator
matrices`.
graph semantics
As with :mod:`scipy.sparse.csgraph`, explicit zeros are
interpreted as the number 0, but implicit zeros indicate a masked
or absent value, such as the absence of an edge between two
vertices of a graph, where an explicit value indicates an edge's
weight. This interpretation is adopted to represent connectivity
in clustering, in representations of nearest neighborhoods
(e.g. :func:`neighbors.kneighbors_graph`), and for precomputed
distance representation where only distances in the neighborhood
of each point are required.
When working with sparse matrices, we assume that it is sparse for a
good reason, and avoid writing code that densifies a user-provided
sparse matrix, instead maintaining sparsity or raising an error if not
possible (i.e. if an estimator does not / cannot support sparse
matrices).
stateless
An estimator is stateless if it does not store any information that is
obtained during :term:`fit`. This information can be either parameters
learned during :term:`fit` or statistics computed from the
training data. An estimator is stateless if it has no :term:`attributes`
apart from ones set in `__init__`. Calling :term:`fit` for these
estimators will only validate the public :term:`attributes` passed
in `__init__`.
supervised
supervised learning
Learning where the expected prediction (label or ground truth) is
available for each sample when :term:`fitting` the model, provided as
:term:`y`. This is the approach taken in a :term:`classifier` or
:term:`regressor` among other estimators.
target
targets
The *dependent variable* in :term:`supervised` (and
:term:`semisupervised`) learning, passed as :term:`y` to an estimator's
:term:`fit` method. Also known as *dependent variable*, *outcome
variable*, *response variable*, *ground truth* or *label*. Scikit-learn
works with targets that have minimal structure: a class from a finite
set, a finite real-valued number, multiple classes, or multiple
numbers. See :ref:`glossary_target_types`.
transduction
transductive
A transductive (contrasted with :term:`inductive`) machine learning
method is designed to model a specific dataset, but not to apply that
model to unseen data. Examples include :class:`manifold.TSNE`,
:class:`cluster.AgglomerativeClustering` and
:class:`neighbors.LocalOutlierFactor`.
unlabeled
unlabeled data
Samples with an unknown ground truth when fitting; equivalently,
:term:`missing values` in the :term:`target`. See also
:term:`semisupervised` and :term:`unsupervised` learning.
unsupervised
unsupervised learning
Learning where the expected prediction (label or ground truth) is not
available for each sample when :term:`fitting` the model, as in
:term:`clusterers` and :term:`outlier detectors`. Unsupervised
estimators ignore any :term:`y` passed to :term:`fit`.
.. _glossary_estimator_types:
Class APIs and Estimator Types
==============================
.. glossary::
classifier
classifiers
A :term:`supervised` (or :term:`semi-supervised`) :term:`predictor`
with a finite set of discrete possible output values.
A classifier supports modeling some of :term:`binary`,
:term:`multiclass`, :term:`multilabel`, or :term:`multiclass
multioutput` targets. Within scikit-learn, all classifiers support
multi-class classification, defaulting to using a one-vs-rest
strategy over the binary classification problem.
Classifiers must store a :term:`classes_` attribute after fitting,
and usually inherit from :class:`base.ClassifierMixin`, which sets
their :term:`_estimator_type` attribute.
A classifier can be distinguished from other estimators with
:func:`~base.is_classifier`.
A classifier must implement:
* :term:`fit`
* :term:`predict`
* :term:`score`
It may also be appropriate to implement :term:`decision_function`,
:term:`predict_proba` and :term:`predict_log_proba`.
clusterer
clusterers
A :term:`unsupervised` :term:`predictor` with a finite set of discrete
output values.
A clusterer usually stores :term:`labels_` after fitting, and must do
so if it is :term:`transductive`.
A clusterer must implement:
* :term:`fit`
* :term:`fit_predict` if :term:`transductive`
* :term:`predict` if :term:`inductive`
density estimator
An :term:`unsupervised` estimation of input probability density
function. Commonly used techniques are:
* :ref:`kernel_density` - uses a kernel function, controlled by the
bandwidth parameter to represent density;
* :ref:`Gaussian mixture ` - uses mixture of Gaussian models
to represent density.
estimator
estimators
An object which manages the estimation and decoding of a model. The
model is estimated as a deterministic function of:
* :term:`parameters` provided in object construction or with
:term:`set_params`;
* the global :mod:`numpy.random` random state if the estimator's
:term:`random_state` parameter is set to None; and
* any data or :term:`sample properties` passed to the most recent
call to :term:`fit`, :term:`fit_transform` or :term:`fit_predict`,
or data similarly passed in a sequence of calls to
:term:`partial_fit`.
The estimated model is stored in public and private :term:`attributes`
on the estimator instance, facilitating decoding through prediction
and transformation methods.
Estimators must provide a :term:`fit` method, and should provide
:term:`set_params` and :term:`get_params`, although these are usually
provided by inheritance from :class:`base.BaseEstimator`.
The core functionality of some estimators may also be available as a
:term:`function`.
feature extractor
feature extractors
A :term:`transformer` which takes input where each sample is not
represented as an :term:`array-like` object of fixed length, and
produces an :term:`array-like` object of :term:`features` for each
sample (and thus a 2-dimensional array-like for a set of samples). In
other words, it (lossily) maps a non-rectangular data representation
into :term:`rectangular` data.
Feature extractors must implement at least:
* :term:`fit`
* :term:`transform`
* :term:`get_feature_names_out`
meta-estimator
meta-estimators
metaestimator
metaestimators
An :term:`estimator` which takes another estimator as a parameter.
Examples include :class:`pipeline.Pipeline`,
:class:`model_selection.GridSearchCV`,
:class:`feature_selection.SelectFromModel` and
:class:`ensemble.BaggingClassifier`.
In a meta-estimator's :term:`fit` method, any contained estimators
should be :term:`cloned` before they are fit (although FIXME: Pipeline
and FeatureUnion do not do this currently). An exception to this is
that an estimator may explicitly document that it accepts a pre-fitted
estimator (e.g. using ``prefit=True`` in
:class:`feature_selection.SelectFromModel`). One known issue with this
is that the pre-fitted estimator will lose its model if the
meta-estimator is cloned. A meta-estimator should have ``fit`` called
before prediction, even if all contained estimators are pre-fitted.
In cases where a meta-estimator's primary behaviors (e.g.
:term:`predict` or :term:`transform` implementation) are functions of
prediction/transformation methods of the provided *base estimator* (or
multiple base estimators), a meta-estimator should provide at least the
standard methods provided by the base estimator. It may not be
possible to identify which methods are provided by the underlying
estimator until the meta-estimator has been :term:`fitted` (see also
:term:`duck typing`), for which
:func:`utils.metaestimators.available_if` may help. It
should also provide (or modify) the :term:`estimator tags` and
:term:`classes_` attribute provided by the base estimator.
Meta-estimators should be careful to validate data as minimally as
possible before passing it to an underlying estimator. This saves
computation time, and may, for instance, allow the underlying
estimator to easily work with data that is not :term:`rectangular`.
outlier detector
outlier detectors
An :term:`unsupervised` binary :term:`predictor` which models the
distinction between core and outlying samples.
Outlier detectors must implement:
* :term:`fit`
* :term:`fit_predict` if :term:`transductive`
* :term:`predict` if :term:`inductive`
Inductive outlier detectors may also implement
:term:`decision_function` to give a normalized inlier score where
outliers have score below 0. :term:`score_samples` may provide an
unnormalized score per sample.
predictor
predictors
An :term:`estimator` supporting :term:`predict` and/or
:term:`fit_predict`. This encompasses :term:`classifier`,
:term:`regressor`, :term:`outlier detector` and :term:`clusterer`.
In statistics, "predictors" refers to :term:`features`.
regressor
regressors
A :term:`supervised` (or :term:`semi-supervised`) :term:`predictor`
with :term:`continuous` output values.
Regressors usually inherit from :class:`base.RegressorMixin`, which
sets their :term:`_estimator_type` attribute.
A regressor can be distinguished from other estimators with
:func:`~base.is_regressor`.
A regressor must implement:
* :term:`fit`
* :term:`predict`
* :term:`score`
transformer
transformers
An estimator supporting :term:`transform` and/or :term:`fit_transform`.
A purely :term:`transductive` transformer, such as
:class:`manifold.TSNE`, may not implement ``transform``.
vectorizer
vectorizers
See :term:`feature extractor`.
There are further APIs specifically related to a small family of estimators,
such as:
.. glossary::
cross-validation splitter
CV splitter
cross-validation generator
A non-estimator family of classes used to split a dataset into a
sequence of train and test portions (see :ref:`cross_validation`),
by providing :term:`split` and :term:`get_n_splits` methods.
Note that unlike estimators, these do not have :term:`fit` methods
and do not provide :term:`set_params` or :term:`get_params`.
Parameter validation may be performed in ``__init__``.
cross-validation estimator
An estimator that has built-in cross-validation capabilities to
automatically select the best hyper-parameters (see the :ref:`User
Guide `). Some example of cross-validation estimators
are :class:`ElasticNetCV ` and
:class:`LogisticRegressionCV `.
Cross-validation estimators are named `EstimatorCV` and tend to be
roughly equivalent to `GridSearchCV(Estimator(), ...)`. The
advantage of using a cross-validation estimator over the canonical
:term:`estimator` class along with :ref:`grid search ` is
that they can take advantage of warm-starting by reusing precomputed
results in the previous steps of the cross-validation process. This
generally leads to speed improvements. An exception is the
:class:`RidgeCV ` class, which can instead
perform efficient Leave-One-Out (LOO) CV. By default, all these
estimators, apart from :class:`RidgeCV ` with an
LOO-CV, will be refitted on the full training dataset after finding the
best combination of hyper-parameters.
scorer
A non-estimator callable object which evaluates an estimator on given
test data, returning a number. Unlike :term:`evaluation metrics`,
a greater returned number must correspond with a *better* score.
See :ref:`scoring_parameter`.
Further examples:
* :class:`metrics.DistanceMetric`
* :class:`gaussian_process.kernels.Kernel`
* ``tree.Criterion``
.. _glossary_metadata_routing:
Metadata Routing
================
.. glossary::
consumer
An object which consumes :term:`metadata`. This object is usually an
:term:`estimator`, a :term:`scorer`, or a :term:`CV splitter`. Consuming
metadata means using it in calculations, e.g. using
:term:`sample_weight` to calculate a certain type of score. Being a
consumer doesn't mean that the object always receives a certain
metadata, rather it means it can use it if it is provided.
metadata
Data which is related to the given :term:`X` and :term:`y` data, but
is not directly a part of the data, e.g. :term:`sample_weight` or
:term:`groups`, and is passed along to different objects and methods,
e.g. to a :term:`scorer` or a :term:`CV splitter`.
router
An object which routes metadata to :term:`consumers `. This
object is usually a :term:`meta-estimator`, e.g.
:class:`~pipeline.Pipeline` or :class:`~model_selection.GridSearchCV`.
Some routers can also be a consumer. This happens for example when a
meta-estimator uses the given :term:`groups`, and it also passes it
along to some of its sub-objects, such as a :term:`CV splitter`.
Please refer to :ref:`Metadata Routing User Guide ` for more
information.
.. _glossary_target_types:
Target Types
============
.. glossary::
binary
A classification problem consisting of two classes. A binary target
may be represented as for a :term:`multiclass` problem but with only two
labels. A binary decision function is represented as a 1d array.
Semantically, one class is often considered the "positive" class.
Unless otherwise specified (e.g. using :term:`pos_label` in
:term:`evaluation metrics`), we consider the class label with the
greater value (numerically or lexicographically) as the positive class:
of labels [0, 1], 1 is the positive class; of [1, 2], 2 is the positive
class; of ['no', 'yes'], 'yes' is the positive class; of ['no', 'YES'],
'no' is the positive class. This affects the output of
:term:`decision_function`, for instance.
Note that a dataset sampled from a multiclass ``y`` or a continuous
``y`` may appear to be binary.
:func:`~utils.multiclass.type_of_target` will return 'binary' for
binary input, or a similar array with only a single class present.
continuous
A regression problem where each sample's target is a finite floating
point number represented as a 1-dimensional array of floats (or
sometimes ints).
:func:`~utils.multiclass.type_of_target` will return 'continuous' for
continuous input, but if the data is all integers, it will be
identified as 'multiclass'.
continuous multioutput
continuous multi-output
multioutput continuous
multi-output continuous
A regression problem where each sample's target consists of ``n_outputs``
:term:`outputs`, each one a finite floating point number, for a
fixed int ``n_outputs > 1`` in a particular dataset.
Continuous multioutput targets are represented as multiple
:term:`continuous` targets, horizontally stacked into an array
of shape ``(n_samples, n_outputs)``.
:func:`~utils.multiclass.type_of_target` will return
'continuous-multioutput' for continuous multioutput input, but if the
data is all integers, it will be identified as
'multiclass-multioutput'.
multiclass
multi-class
A classification problem consisting of more than two classes. A
multiclass target may be represented as a 1-dimensional array of
strings or integers. A 2d column vector of integers (i.e. a
single output in :term:`multioutput` terms) is also accepted.
We do not officially support other orderable, hashable objects as class
labels, even if estimators may happen to work when given classification
targets of such type.
For semi-supervised classification, :term:`unlabeled` samples should
have the special label -1 in ``y``.
Within scikit-learn, all estimators supporting binary classification
also support multiclass classification, using One-vs-Rest by default.
A :class:`preprocessing.LabelEncoder` helps to canonicalize multiclass
targets as integers.
:func:`~utils.multiclass.type_of_target` will return 'multiclass' for
multiclass input. The user may also want to handle 'binary' input
identically to 'multiclass'.
multiclass multioutput
multi-class multi-output
multioutput multiclass
multi-output multi-class
A classification problem where each sample's target consists of
``n_outputs`` :term:`outputs`, each a class label, for a fixed int
``n_outputs > 1`` in a particular dataset. Each output has a
fixed set of available classes, and each sample is labeled with a
class for each output. An output may be binary or multiclass, and in
the case where all outputs are binary, the target is
:term:`multilabel`.
Multiclass multioutput targets are represented as multiple
:term:`multiclass` targets, horizontally stacked into an array
of shape ``(n_samples, n_outputs)``.
XXX: For simplicity, we may not always support string class labels
for multiclass multioutput, and integer class labels should be used.
:mod:`~sklearn.multioutput` provides estimators which estimate multi-output
problems using multiple single-output estimators. This may not fully
account for dependencies among the different outputs, which methods
natively handling the multioutput case (e.g. decision trees, nearest
neighbors, neural networks) may do better.
:func:`~utils.multiclass.type_of_target` will return
'multiclass-multioutput' for multiclass multioutput input.
multilabel
multi-label
A :term:`multiclass multioutput` target where each output is
:term:`binary`. This may be represented as a 2d (dense) array or
sparse matrix of integers, such that each column is a separate binary
target, where positive labels are indicated with 1 and negative labels
are usually -1 or 0. Sparse multilabel targets are not supported
everywhere that dense multilabel targets are supported.
Semantically, a multilabel target can be thought of as a set of labels
for each sample. While not used internally,
:class:`preprocessing.MultiLabelBinarizer` is provided as a utility to
convert from a list of sets representation to a 2d array or sparse
matrix. One-hot encoding a multiclass target with
:class:`preprocessing.LabelBinarizer` turns it into a multilabel
problem.
:func:`~utils.multiclass.type_of_target` will return
'multilabel-indicator' for multilabel input, whether sparse or dense.
multioutput
multi-output
A target where each sample has multiple classification/regression
labels. See :term:`multiclass multioutput` and :term:`continuous
multioutput`. We do not currently support modelling mixed
classification and regression targets.
.. _glossary_methods:
Methods
=======
.. glossary::
``decision_function``
In a fitted :term:`classifier` or :term:`outlier detector`, predicts a
"soft" score for each sample in relation to each class, rather than the
"hard" categorical prediction produced by :term:`predict`. Its input
is usually only some observed data, :term:`X`.
If the estimator was not already :term:`fitted`, calling this method
should raise a :class:`exceptions.NotFittedError`.
Output conventions:
binary classification
A 1-dimensional array, where values strictly greater than zero
indicate the positive class (i.e. the last class in
:term:`classes_`).
multiclass classification
A 2-dimensional array, where the row-wise arg-maximum is the
predicted class. Columns are ordered according to
:term:`classes_`.
multilabel classification
Scikit-learn is inconsistent in its representation of :term:`multilabel`
decision functions. It may be represented one of two ways:
- List of 2d arrays, each array of shape: (`n_samples`, 2), like in
multiclass multioutput. List is of length `n_labels`.
- Single 2d array of shape (`n_samples`, `n_labels`), with each
'column' in the array corresponding to the individual binary
classification decisions. This is identical to the
multiclass classification format, though its semantics differ: it
should be interpreted, like in the binary case, by thresholding at
0.
multioutput classification
A list of 2d arrays, corresponding to each multiclass decision
function.
outlier detection
A 1-dimensional array, where a value greater than or equal to zero
indicates an inlier.
``fit``
The ``fit`` method is provided on every estimator. It usually takes some
:term:`samples` ``X``, :term:`targets` ``y`` if the model is supervised,
and potentially other :term:`sample properties` such as
:term:`sample_weight`. It should:
* clear any prior :term:`attributes` stored on the estimator, unless
:term:`warm_start` is used;
* validate and interpret any :term:`parameters`, ideally raising an
error if invalid;
* validate the input data;
* estimate and store model attributes from the estimated parameters and
provided data; and
* return the now :term:`fitted` estimator to facilitate method
chaining.
:ref:`glossary_target_types` describes possible formats for ``y``.
``fit_predict``
Used especially for :term:`unsupervised`, :term:`transductive`
estimators, this fits the model and returns the predictions (similar to
:term:`predict`) on the training data. In clusterers, these predictions
are also stored in the :term:`labels_` attribute, and the output of
``.fit_predict(X)`` is usually equivalent to ``.fit(X).predict(X)``.
The parameters to ``fit_predict`` are the same as those to ``fit``.
``fit_transform``
A method on :term:`transformers` which fits the estimator and returns
the transformed training data. It takes parameters as in :term:`fit`
and its output should have the same shape as calling ``.fit(X,
...).transform(X)``. There are nonetheless rare cases where
``.fit_transform(X, ...)`` and ``.fit(X, ...).transform(X)`` do not
return the same value, wherein training data needs to be handled
differently (due to model blending in stacked ensembles, for instance;
such cases should be clearly documented).
:term:`Transductive ` transformers may also provide
``fit_transform`` but not :term:`transform`.
One reason to implement ``fit_transform`` is that performing ``fit``
and ``transform`` separately would be less efficient than together.
:class:`base.TransformerMixin` provides a default implementation,
providing a consistent interface across transformers where
``fit_transform`` is or is not specialized.
In :term:`inductive` learning -- where the goal is to learn a
generalized model that can be applied to new data -- users should be
careful not to apply ``fit_transform`` to the entirety of a dataset
(i.e. training and test data together) before further modelling, as
this results in :term:`data leakage`.
``get_feature_names_out``
Primarily for :term:`feature extractors`, but also used for other
transformers to provide string names for each column in the output of
the estimator's :term:`transform` method. It outputs an array of
strings and may take an array-like of strings as input, corresponding
to the names of input columns from which output column names can
be generated. If `input_features` is not passed in, then the
`feature_names_in_` attribute will be used. If the
`feature_names_in_` attribute is not defined, then the
input names are named `[x0, x1, ..., x(n_features_in_ - 1)]`.
``get_n_splits``
On a :term:`CV splitter` (not an estimator), returns the number of
elements one would get if iterating through the return value of
:term:`split` given the same parameters. Takes the same parameters as
split.
``get_params``
Gets all :term:`parameters`, and their values, that can be set using
:term:`set_params`. A parameter ``deep`` can be used, when set to
False to only return those parameters not including ``__``, i.e. not
due to indirection via contained estimators.
Most estimators adopt the definition from :class:`base.BaseEstimator`,
which simply adopts the parameters defined for ``__init__``.
:class:`pipeline.Pipeline`, among others, reimplements ``get_params``
to declare the estimators named in its ``steps`` parameters as
themselves being parameters.
``partial_fit``
Facilitates fitting an estimator in an online fashion. Unlike ``fit``,
repeatedly calling ``partial_fit`` does not clear the model, but
updates it with the data provided. The portion of data
provided to ``partial_fit`` may be called a mini-batch.
Each mini-batch must be of consistent shape, etc. In iterative
estimators, ``partial_fit`` often only performs a single iteration.
``partial_fit`` may also be used for :term:`out-of-core` learning,
although usually limited to the case where learning can be performed
online, i.e. the model is usable after each ``partial_fit`` and there
is no separate processing needed to finalize the model.
:class:`cluster.Birch` introduces the convention that calling
``partial_fit(X)`` will produce a model that is not finalized, but the
model can be finalized by calling ``partial_fit()`` i.e. without
passing a further mini-batch.
Generally, estimator parameters should not be modified between calls
to ``partial_fit``, although ``partial_fit`` should validate them
as well as the new mini-batch of data. In contrast, ``warm_start``
is used to repeatedly fit the same estimator with the same data
but varying parameters.
Like ``fit``, ``partial_fit`` should return the estimator object.
To clear the model, a new estimator should be constructed, for instance
with :func:`base.clone`.
NOTE: Using ``partial_fit`` after ``fit`` results in undefined behavior.
``predict``
Makes a prediction for each sample, usually only taking :term:`X` as
input (but see under regressor output conventions below). In a
:term:`classifier` or :term:`regressor`, this prediction is in the same
target space used in fitting (e.g. one of {'red', 'amber', 'green'} if
the ``y`` in fitting consisted of these strings). Despite this, even
when ``y`` passed to :term:`fit` is a list or other array-like, the
output of ``predict`` should always be an array or sparse matrix. In a
:term:`clusterer` or :term:`outlier detector` the prediction is an
integer.
If the estimator was not already :term:`fitted`, calling this method
should raise a :class:`exceptions.NotFittedError`.
Output conventions:
classifier
An array of shape ``(n_samples,)`` ``(n_samples, n_outputs)``.
:term:`Multilabel ` data may be represented as a sparse
matrix if a sparse matrix was used in fitting. Each element should
be one of the values in the classifier's :term:`classes_`
attribute.
clusterer
An array of shape ``(n_samples,)`` where each value is from 0 to
``n_clusters - 1`` if the corresponding sample is clustered,
and -1 if the sample is not clustered, as in
:func:`cluster.dbscan`.
outlier detector
An array of shape ``(n_samples,)`` where each value is -1 for an
outlier and 1 otherwise.
regressor
A numeric array of shape ``(n_samples,)``, usually float64.
Some regressors have extra options in their ``predict`` method,
allowing them to return standard deviation (``return_std=True``)
or covariance (``return_cov=True``) relative to the predicted
value. In this case, the return value is a tuple of arrays
corresponding to (prediction mean, std, cov) as required.
``predict_log_proba``
The natural logarithm of the output of :term:`predict_proba`, provided
to facilitate numerical stability.
``predict_proba``
A method in :term:`classifiers` and :term:`clusterers` that can
return probability estimates for each class/cluster. Its input is
usually only some observed data, :term:`X`.
If the estimator was not already :term:`fitted`, calling this method
should raise a :class:`exceptions.NotFittedError`.
Output conventions are like those for :term:`decision_function` except
in the :term:`binary` classification case, where one column is output
for each class (while ``decision_function`` outputs a 1d array). For
binary and multiclass predictions, each row should add to 1.
Like other methods, ``predict_proba`` should only be present when the
estimator can make probabilistic predictions (see :term:`duck typing`).
This means that the presence of the method may depend on estimator
parameters (e.g. in :class:`linear_model.SGDClassifier`) or training
data (e.g. in :class:`model_selection.GridSearchCV`) and may only
appear after fitting.
``score``
A method on an estimator, usually a :term:`predictor`, which evaluates
its predictions on a given dataset, and returns a single numerical
score. A greater return value should indicate better predictions;
accuracy is used for classifiers and R^2 for regressors by default.
If the estimator was not already :term:`fitted`, calling this method
should raise a :class:`exceptions.NotFittedError`.
Some estimators implement a custom, estimator-specific score function,
often the likelihood of the data under the model.
``score_samples``
A method that returns a score for each given sample. The exact
definition of *score* varies from one class to another. In the case of
density estimation, it can be the log density model on the data, and in
the case of outlier detection, it can be the opposite of the outlier
factor of the data.
If the estimator was not already :term:`fitted`, calling this method
should raise a :class:`exceptions.NotFittedError`.
``set_params``
Available in any estimator, takes keyword arguments corresponding to
keys in :term:`get_params`. Each is provided a new value to assign
such that calling ``get_params`` after ``set_params`` will reflect the
changed :term:`parameters`. Most estimators use the implementation in
:class:`base.BaseEstimator`, which handles nested parameters and
otherwise sets the parameter as an attribute on the estimator.
The method is overridden in :class:`pipeline.Pipeline` and related
estimators.
``split``
On a :term:`CV splitter` (not an estimator), this method accepts
parameters (:term:`X`, :term:`y`, :term:`groups`), where all may be
optional, and returns an iterator over ``(train_idx, test_idx)``
pairs. Each of {train,test}_idx is a 1d integer array, with values
from 0 from ``X.shape[0] - 1`` of any length, such that no values
appear in both some ``train_idx`` and its corresponding ``test_idx``.
``transform``
In a :term:`transformer`, transforms the input, usually only :term:`X`,
into some transformed space (conventionally notated as :term:`Xt`).
Output is an array or sparse matrix of length :term:`n_samples` and
with the number of columns fixed after :term:`fitting`.
If the estimator was not already :term:`fitted`, calling this method
should raise a :class:`exceptions.NotFittedError`.
.. _glossary_parameters:
Parameters
==========
These common parameter names, specifically used in estimator construction
(see concept :term:`parameter`), sometimes also appear as parameters of
functions or non-estimator constructors.
.. glossary::
``class_weight``
Used to specify sample weights when fitting classifiers as a function
of the :term:`target` class. Where :term:`sample_weight` is also
supported and given, it is multiplied by the ``class_weight``
contribution. Similarly, where ``class_weight`` is used in a
:term:`multioutput` (including :term:`multilabel`) tasks, the weights
are multiplied across outputs (i.e. columns of ``y``).
By default, all samples have equal weight such that classes are
effectively weighted by their prevalence in the training data.
This could be achieved explicitly with ``class_weight={label1: 1,
label2: 1, ...}`` for all class labels.
More generally, ``class_weight`` is specified as a dict mapping class
labels to weights (``{class_label: weight}``), such that each sample
of the named class is given that weight.
``class_weight='balanced'`` can be used to give all classes
equal weight by giving each sample a weight inversely related
to its class's prevalence in the training data:
``n_samples / (n_classes * np.bincount(y))``. Class weights will be
used differently depending on the algorithm: for linear models (such
as linear SVM or logistic regression), the class weights will alter the
loss function by weighting the loss of each sample by its class weight.
For tree-based algorithms, the class weights will be used for
reweighting the splitting criterion.
**Note** however that this rebalancing does not take the weight of
samples in each class into account.
For multioutput classification, a list of dicts is used to specify
weights for each output. For example, for four-class multilabel
classification weights should be ``[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1,
1: 1}, {0: 1, 1: 1}]`` instead of ``[{1:1}, {2:5}, {3:1}, {4:1}]``.
The ``class_weight`` parameter is validated and interpreted with
:func:`utils.class_weight.compute_class_weight`.
``cv``
Determines a cross validation splitting strategy, as used in
cross-validation based routines. ``cv`` is also available in estimators
such as :class:`multioutput.ClassifierChain` or
:class:`calibration.CalibratedClassifierCV` which use the predictions
of one estimator as training data for another, to not overfit the
training supervision.
Possible inputs for ``cv`` are usually:
- An integer, specifying the number of folds in K-fold cross
validation. K-fold will be stratified over classes if the estimator
is a classifier (determined by :func:`base.is_classifier`) and the
:term:`targets` may represent a binary or multiclass (but not
multioutput) classification problem (determined by
:func:`utils.multiclass.type_of_target`).
- A :term:`cross-validation splitter` instance. Refer to the
:ref:`User Guide ` for splitters available
within Scikit-learn.
- An iterable yielding train/test splits.
With some exceptions (especially where not using cross validation at
all is an option), the default is 5-fold.
``cv`` values are validated and interpreted with
:func:`model_selection.check_cv`.
``kernel``
Specifies the kernel function to be used by Kernel Method algorithms.
For example, the estimators :class:`svm.SVC` and
:class:`gaussian_process.GaussianProcessClassifier` both have a
``kernel`` parameter that takes the name of the kernel to use as string
or a callable kernel function used to compute the kernel matrix. For
more reference, see the :ref:`kernel_approximation` and the
:ref:`gaussian_process` user guides.
``max_iter``
For estimators involving iterative optimization, this determines the
maximum number of iterations to be performed in :term:`fit`. If
``max_iter`` iterations are run without convergence, a
:class:`exceptions.ConvergenceWarning` should be raised. Note that the
interpretation of "a single iteration" is inconsistent across
estimators: some, but not all, use it to mean a single epoch (i.e. a
pass over every sample in the data).
FIXME perhaps we should have some common tests about the relationship
between ConvergenceWarning and max_iter.
``memory``
Some estimators make use of :class:`joblib.Memory` to
store partial solutions during fitting. Thus when ``fit`` is called
again, those partial solutions have been memoized and can be reused.
A ``memory`` parameter can be specified as a string with a path to a
directory, or a :class:`joblib.Memory` instance (or an object with a
similar interface, i.e. a ``cache`` method) can be used.
``memory`` values are validated and interpreted with
:func:`utils.validation.check_memory`.
``metric``
As a parameter, this is the scheme for determining the distance between
two data points. See :func:`metrics.pairwise_distances`. In practice,
for some algorithms, an improper distance metric (one that does not
obey the triangle inequality, such as Cosine Distance) may be used.
XXX: hierarchical clustering uses ``affinity`` with this meaning.
We also use *metric* to refer to :term:`evaluation metrics`, but avoid
using this sense as a parameter name.
``n_components``
The number of features which a :term:`transformer` should transform the
input into. See :term:`components_` for the special case of affine
projection.
``n_iter_no_change``
Number of iterations with no improvement to wait before stopping the
iterative procedure. This is also known as a *patience* parameter. It
is typically used with :term:`early stopping` to avoid stopping too
early.
``n_jobs``
This parameter is used to specify how many concurrent processes or
threads should be used for routines that are parallelized with
:term:`joblib`.
``n_jobs`` is an integer, specifying the maximum number of concurrently
running workers. If 1 is given, no joblib parallelism is used at all,
which is useful for debugging. If set to -1, all CPUs are used. For
``n_jobs`` below -1, (n_cpus + 1 + n_jobs) are used. For example with
``n_jobs=-2``, all CPUs but one are used.
``n_jobs`` is ``None`` by default, which means *unset*; it will
generally be interpreted as ``n_jobs=1``, unless the current
:class:`joblib.Parallel` backend context specifies otherwise.
Note that even if ``n_jobs=1``, low-level parallelism (via Numpy and OpenMP)
might be used in some configuration.
For more details on the use of ``joblib`` and its interactions with
scikit-learn, please refer to our :ref:`parallelism notes
`.
``pos_label``
Value with which positive labels must be encoded in binary
classification problems in which the positive class is not assumed.
This value is typically required to compute asymmetric evaluation
metrics such as precision and recall.
``random_state``
Whenever randomization is part of a Scikit-learn algorithm, a
``random_state`` parameter may be provided to control the random number
generator used. Note that the mere presence of ``random_state`` doesn't
mean that randomization is always used, as it may be dependent on
another parameter, e.g. ``shuffle``, being set.
The passed value will have an effect on the reproducibility of the
results returned by the function (:term:`fit`, :term:`split`, or any
other function like :func:`~sklearn.cluster.k_means`). `random_state`'s
value may be:
None (default)
Use the global random state instance from :mod:`numpy.random`.
Calling the function multiple times will reuse
the same instance, and will produce different results.
An integer
Use a new random number generator seeded by the given integer.
Using an int will produce the same results across different calls.
However, it may be
worthwhile checking that your results are stable across a
number of different distinct random seeds. Popular integer
random seeds are 0 and `42
`_.
Integer values must be in the range `[0, 2**32 - 1]`.
A :class:`numpy.random.RandomState` instance
Use the provided random state, only affecting other users
of that same random state instance. Calling the function
multiple times will reuse the same instance, and
will produce different results.
:func:`utils.check_random_state` is used internally to validate the
input ``random_state`` and return a :class:`~numpy.random.RandomState`
instance.
For more details on how to control the randomness of scikit-learn
objects and avoid common pitfalls, you may refer to :ref:`randomness`.
``scoring``
Specifies the score function to be maximized (usually by :ref:`cross
validation `), or -- in some cases -- multiple score
functions to be reported. The score function can be a string accepted
by :func:`metrics.get_scorer` or a callable :term:`scorer`, not to be
confused with an :term:`evaluation metric`, as the latter have a more
diverse API. ``scoring`` may also be set to None, in which case the
estimator's :term:`score` method is used. See :ref:`scoring_parameter`
in the User Guide.
Where multiple metrics can be evaluated, ``scoring`` may be given
either as a list of unique strings, a dictionary with names as keys and
callables as values or a callable that returns a dictionary. Note that
this does *not* specify which score function is to be maximized, and
another parameter such as ``refit`` maybe used for this purpose.
The ``scoring`` parameter is validated and interpreted using
:func:`metrics.check_scoring`.
``verbose``
Logging is not handled very consistently in Scikit-learn at present,
but when it is provided as an option, the ``verbose`` parameter is
usually available to choose no logging (set to False). Any True value
should enable some logging, but larger integers (e.g. above 10) may be
needed for full verbosity. Verbose logs are usually printed to
Standard Output.
Estimators should not produce any output on Standard Output with the
default ``verbose`` setting.
``warm_start``
When fitting an estimator repeatedly on the same dataset, but for
multiple parameter values (such as to find the value maximizing
performance as in :ref:`grid search `), it may be possible
to reuse aspects of the model learned from the previous parameter value,
saving time. When ``warm_start`` is true, the existing :term:`fitted`
model :term:`attributes` are used to initialize the new model
in a subsequent call to :term:`fit`.
Note that this is only applicable for some models and some
parameters, and even some orders of parameter values. In general, there
is an interaction between ``warm_start`` and the parameter controlling
the number of iterations of the estimator.
For estimators imported from :mod:`~sklearn.ensemble`,
``warm_start`` will interact with ``n_estimators`` or ``max_iter``.
For these models, the number of iterations, reported via
``len(estimators_)`` or ``n_iter_``, corresponds the total number of
estimators/iterations learnt since the initialization of the model.
Thus, if a model was already initialized with `N` estimators, and `fit`
is called with ``n_estimators`` or ``max_iter`` set to `M`, the model
will train `M - N` new estimators.
Other models, usually using gradient-based solvers, have a different
behavior. They all expose a ``max_iter`` parameter. The reported
``n_iter_`` corresponds to the number of iteration done during the last
call to ``fit`` and will be at most ``max_iter``. Thus, we do not
consider the state of the estimator since the initialization.
:term:`partial_fit` also retains the model between calls, but differs:
with ``warm_start`` the parameters change and the data is
(more-or-less) constant across calls to ``fit``; with ``partial_fit``,
the mini-batch of data changes and model parameters stay fixed.
There are cases where you want to use ``warm_start`` to fit on
different, but closely related data. For example, one may initially fit
to a subset of the data, then fine-tune the parameter search on the
full dataset. For classification, all data in a sequence of
``warm_start`` calls to ``fit`` must include samples from each class.
.. _glossary_attributes:
Attributes
==========
See concept :term:`attribute`.
.. glossary::
``classes_``
A list of class labels known to the :term:`classifier`, mapping each
label to a numerical index used in the model representation our output.
For instance, the array output from :term:`predict_proba` has columns
aligned with ``classes_``. For :term:`multi-output` classifiers,
``classes_`` should be a list of lists, with one class listing for
each output. For each output, the classes should be sorted
(numerically, or lexicographically for strings).
``classes_`` and the mapping to indices is often managed with
:class:`preprocessing.LabelEncoder`.
``components_``
An affine transformation matrix of shape ``(n_components, n_features)``
used in many linear :term:`transformers` where :term:`n_components` is
the number of output features and :term:`n_features` is the number of
input features.
See also :term:`components_` which is a similar attribute for linear
predictors.
``coef_``
The weight/coefficient matrix of a generalized linear model
:term:`predictor`, of shape ``(n_features,)`` for binary classification
and single-output regression, ``(n_classes, n_features)`` for
multiclass classification and ``(n_targets, n_features)`` for
multi-output regression. Note this does not include the intercept
(or bias) term, which is stored in ``intercept_``.
When available, ``feature_importances_`` is not usually provided as
well, but can be calculated as the norm of each feature's entry in
``coef_``.
See also :term:`components_` which is a similar attribute for linear
transformers.
``embedding_``
An embedding of the training data in :ref:`manifold learning
` estimators, with shape ``(n_samples, n_components)``,
identical to the output of :term:`fit_transform`. See also
:term:`labels_`.
``n_iter_``
The number of iterations actually performed when fitting an iterative
estimator that may stop upon convergence. See also :term:`max_iter`.
``feature_importances_``
A vector of shape ``(n_features,)`` available in some
:term:`predictors` to provide a relative measure of the importance of
each feature in the predictions of the model.
``labels_``
A vector containing a cluster label for each sample of the training
data in :term:`clusterers`, identical to the output of
:term:`fit_predict`. See also :term:`embedding_`.
.. _glossary_sample_props:
Data and sample properties
==========================
See concept :term:`sample property`.
.. glossary::
``groups``
Used in cross-validation routines to identify samples that are correlated.
Each value is an identifier such that, in a supporting
:term:`CV splitter`, samples from some ``groups`` value may not
appear in both a training set and its corresponding test set.
See :ref:`group_cv`.
``sample_weight``
A relative weight for each sample. Intuitively, if all weights are
integers, a weighted model or score should be equivalent to that
calculated when repeating the sample the number of times specified in
the weight. Weights may be specified as floats, so that sample weights
are usually equivalent up to a constant positive scaling factor.
FIXME Is this interpretation always the case in practice? We have no
common tests.
Some estimators, such as decision trees, support negative weights.
FIXME: This feature or its absence may not be tested or documented in
many estimators.
This is not entirely the case where other parameters of the model
consider the number of samples in a region, as with ``min_samples`` in
:class:`cluster.DBSCAN`. In this case, a count of samples becomes
to a sum of their weights.
In classification, sample weights can also be specified as a function
of class with the :term:`class_weight` estimator :term:`parameter`.
``X``
Denotes data that is observed at training and prediction time, used as
independent variables in learning. The notation is uppercase to denote
that it is ordinarily a matrix (see :term:`rectangular`).
When a matrix, each sample may be represented by a :term:`feature`
vector, or a vector of :term:`precomputed` (dis)similarity with each
training sample. ``X`` may also not be a matrix, and may require a
:term:`feature extractor` or a :term:`pairwise metric` to turn it into
one before learning a model.
``Xt``
Shorthand for "transformed :term:`X`".
``y``
``Y``
Denotes data that may be observed at training time as the dependent
variable in learning, but which is unavailable at prediction time, and
is usually the :term:`target` of prediction. The notation may be
uppercase to denote that it is a matrix, representing
:term:`multi-output` targets, for instance; but usually we use ``y``
and sometimes do so even when multiple outputs are assumed.