.. currentmodule:: sklearn.model_selection

.. _grid_search:

===========================================
Tuning the hyper-parameters of an estimator
===========================================

Hyper-parameters are parameters that are not directly learnt within estimators.
In scikit-learn they are passed as arguments to the constructor of the
estimator classes. Typical examples include ``C``, ``kernel`` and ``gamma``
for Support Vector Classifier, ``alpha`` for Lasso, etc.

It is possible and recommended to search the hyper-parameter space for the
best :ref:`cross validation <cross_validation>` score.

Any parameter provided when constructing an estimator may be optimized in this
manner. Specifically, to find the names and current values for all parameters
for a given estimator, use::

  estimator.get_params()

A search consists of:

- an estimator (regressor or classifier such as ``sklearn.svm.SVC()``);
- a parameter space;
- a method for searching or sampling candidates;
- a cross-validation scheme; and
- a :ref:`score function <gridsearch_scoring>`.

Two generic approaches to parameter search are provided in
scikit-learn: for given values, :class:`GridSearchCV` exhaustively considers
all parameter combinations, while :class:`RandomizedSearchCV` can sample a
given number of candidates from a parameter space with a specified
distribution. Both these tools have successive halving counterparts
:class:`HalvingGridSearchCV` and :class:`HalvingRandomSearchCV`, which can be
much faster at finding a good parameter combination.

After describing these tools we detail :ref:`best practices
<grid_search_tips>` applicable to these approaches. Some models allow for
specialized, efficient parameter search strategies, outlined in
:ref:`alternative_cv`.

Note that it is common that a small subset of those parameters can have a large
impact on the predictive or computation performance of the model while others
can be left to their default values. It is recommended to read the docstring of
the estimator class to get a finer understanding of their expected behavior,
possibly by reading the enclosed reference to the literature.

Exhaustive Grid Search
======================

The grid search provided by :class:`GridSearchCV` exhaustively generates
candidates from a grid of parameter values specified with the ``param_grid``
parameter. For instance, the following ``param_grid``::

  param_grid = [
    {'C': [1, 10, 100, 1000], 'kernel': ['linear']},
    {'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001], 'kernel': ['rbf']},
   ]

specifies that two grids should be explored: one with a linear kernel and
C values in [1, 10, 100, 1000], and the second one with an RBF kernel,
and the cross-product of C values ranging in [1, 10, 100, 1000] and gamma
values in [0.001, 0.0001].

The :class:`GridSearchCV` instance implements the usual estimator API: when
"fitting" it on a dataset all the possible combinations of parameter values are
evaluated and the best combination is retained.

.. currentmodule:: sklearn.model_selection

.. topic:: Examples:

    - See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py` for an example of
      Grid Search computation on the digits dataset.

    - See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py` for an example
      of Grid Search coupling parameters from a text documents feature
      extractor (n-gram count vectorizer and TF-IDF transformer) with a
      classifier (here a linear SVM trained with SGD with either elastic
      net or L2 penalty) using a :class:`~sklearn.pipeline.Pipeline` instance.

    - See :ref:`sphx_glr_auto_examples_model_selection_plot_nested_cross_validation_iris.py`
      for an example of Grid Search within a cross validation loop on the iris
      dataset. This is the best practice for evaluating the performance of a
      model with grid search.

    - See :ref:`sphx_glr_auto_examples_model_selection_plot_multi_metric_evaluation.py`
      for an example of :class:`GridSearchCV` being used to evaluate multiple
      metrics simultaneously.

    - See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_refit_callable.py`
      for an example of using ``refit=callable`` interface in
      :class:`GridSearchCV`. The example shows how this interface adds certain
      amount of flexibility in identifying the "best" estimator. This interface
      can also be used in multiple metrics evaluation.

    - See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_stats.py`
      for an example of how to do a statistical comparison on the outputs of
      :class:`GridSearchCV`.

.. _randomized_parameter_search:

Randomized Parameter Optimization
=================================
While using a grid of parameter settings is currently the most widely used
method for parameter optimization, other search methods have more
favorable properties.
:class:`RandomizedSearchCV` implements a randomized search over parameters,
where each setting is sampled from a distribution over possible parameter values.
This has two main benefits over an exhaustive search:

* A budget can be chosen independent of the number of parameters and possible values.
* Adding parameters that do not influence the performance does not decrease efficiency.

Specifying how parameters should be sampled is done using a dictionary, very
similar to specifying parameters for :class:`GridSearchCV`. Additionally,
a computation budget, being the number of sampled candidates or sampling
iterations, is specified using the ``n_iter`` parameter.
For each parameter, either a distribution over possible values or a list of
discrete choices (which will be sampled uniformly) can be specified::

  {'C': scipy.stats.expon(scale=100), 'gamma': scipy.stats.expon(scale=.1),
    'kernel': ['rbf'], 'class_weight':['balanced', None]}

This example uses the ``scipy.stats`` module, which contains many useful
distributions for sampling parameters, such as ``expon``, ``gamma``,
``uniform``, ``loguniform`` or ``randint``.

In principle, any function can be passed that provides a ``rvs`` (random
variate sample) method to sample a value. A call to the ``rvs`` function should
provide independent random samples from possible parameter values on
consecutive calls.

.. warning::

    The distributions in ``scipy.stats`` prior to version scipy 0.16
    do not allow specifying a random state. Instead, they use the global
    numpy random state, that can be seeded via ``np.random.seed`` or set
    using ``np.random.set_state``. However, beginning scikit-learn 0.18,
    the :mod:`sklearn.model_selection` module sets the random state provided
    by the user if scipy >= 0.16 is also available.

For continuous parameters, such as ``C`` above, it is important to specify
a continuous distribution to take full advantage of the randomization. This way,
increasing ``n_iter`` will always lead to a finer search.

A continuous log-uniform random variable is the continuous version of
a log-spaced parameter. For example to specify the equivalent of ``C`` from above,
``loguniform(1, 100)`` can be used instead of ``[1, 10, 100]``.

Mirroring the example above in grid search, we can specify a continuous random
variable that is log-uniformly distributed between ``1e0`` and ``1e3``::

  from sklearn.utils.fixes import loguniform
  {'C': loguniform(1e0, 1e3),
   'gamma': loguniform(1e-4, 1e-3),
   'kernel': ['rbf'],
   'class_weight':['balanced', None]}

.. topic:: Examples:

    * :ref:`sphx_glr_auto_examples_model_selection_plot_randomized_search.py` compares the usage and efficiency
      of randomized search and grid search.

.. topic:: References:

    * Bergstra, J. and Bengio, Y.,
      Random search for hyper-parameter optimization,
      The Journal of Machine Learning Research (2012)

.. _successive_halving_user_guide:

Searching for optimal parameters with successive halving
========================================================

Scikit-learn also provides the :class:`HalvingGridSearchCV` and
:class:`HalvingRandomSearchCV` estimators that can be used to
search a parameter space using successive halving [1]_ [2]_. Successive
halving (SH) is like a tournament among candidate parameter combinations.
SH is an iterative selection process where all candidates (the
parameter combinations) are evaluated with a small amount of resources at
the first iteration. Only some of these candidates are selected for the next
iteration, which will be allocated more resources. For parameter tuning, the
resource is typically the number of training samples, but it can also be an
arbitrary numeric parameter such as `n_estimators` in a random forest.

As illustrated in the figure below, only a subset of candidates
'survive' until the last iteration. These are the candidates that have
consistently ranked among the top-scoring candidates across all iterations.
Each iteration is allocated an increasing amount of resources per candidate,
here the number of samples.

.. figure:: ../auto_examples/model_selection/images/sphx_glr_plot_successive_halving_iterations_001.png
   :target: ../auto_examples/model_selection/plot_successive_halving_iterations.html
   :align: center

We here briefly describe the main parameters, but each parameter and their
interactions are described in more details in the sections below. The
``factor`` (> 1) parameter controls the rate at which the resources grow, and
the rate at which the number of candidates decreases. In each iteration, the
number of resources per candidate is multiplied by ``factor`` and the number
of candidates is divided by the same factor. Along with ``resource`` and
``min_resources``, ``factor`` is the most important parameter to control the
search in our implementation, though a value of 3 usually works well.
``factor`` effectively controls the number of iterations in
:class:`HalvingGridSearchCV` and the number of candidates (by default) and
iterations in :class:`HalvingRandomSearchCV`. ``aggressive_elimination=True``
can also be used if the number of available resources is small. More control
is available through tuning the ``min_resources`` parameter.

These estimators are still **experimental**: their predictions
and their API might change without any deprecation cycle. To use them, you
need to explicitly import ``enable_halving_search_cv``::

  >>> # explicitly require this experimental feature
  >>> from sklearn.experimental import enable_halving_search_cv  # noqa
  >>> # now you can import normally from model_selection
  >>> from sklearn.model_selection import HalvingGridSearchCV
  >>> from sklearn.model_selection import HalvingRandomSearchCV

.. topic:: Examples:

    * :ref:`sphx_glr_auto_examples_model_selection_plot_successive_halving_heatmap.py`
    * :ref:`sphx_glr_auto_examples_model_selection_plot_successive_halving_iterations.py`

Choosing ``min_resources`` and the number of candidates
-------------------------------------------------------

Beside ``factor``, the two main parameters that influence the behaviour of a
successive halving search are the ``min_resources`` parameter, and the
number of candidates (or parameter combinations) that are evaluated.
``min_resources`` is the amount of resources allocated at the first
iteration for each candidate. The number of candidates is specified directly
in :class:`HalvingRandomSearchCV`, and is determined from the ``param_grid``
parameter of :class:`HalvingGridSearchCV`.

Consider a case where the resource is the number of samples, and where we
have 1000 samples. In theory, with ``min_resources=10`` and ``factor=2``, we
are able to run **at most** 7 iterations with the following number of
samples: ``[10, 20, 40, 80, 160, 320, 640]``.

But depending on the number of candidates, we might run less than 7
iterations: if we start with a **small** number of candidates, the last
iteration might use less than 640 samples, which means not using all the
available resources (samples). For example if we start with 5 candidates, we
only need 2 iterations: 5 candidates for the first iteration, then
`5 // 2 = 2` candidates at the second iteration, after which we know which
candidate performs the best (so we don't need a third one). We would only be
using at most 20 samples which is a waste since we have 1000 samples at our
disposal. On the other hand, if we start with a **high** number of
candidates, we might end up with a lot of candidates at the last iteration,
which may not always be ideal: it means that many candidates will run with
the full resources, basically reducing the procedure to standard search.

In the case of :class:`HalvingRandomSearchCV`, the number of candidates is set
by default such that the last iteration uses as much of the available
resources as possible. For :class:`HalvingGridSearchCV`, the number of
candidates is determined by the `param_grid` parameter. Changing the value of
``min_resources`` will impact the number of possible iterations, and as a
result will also have an effect on the ideal number of candidates.

Another consideration when choosing ``min_resources`` is whether or not it
is easy to discriminate between good and bad candidates with a small amount
of resources. For example, if you need a lot of samples to distinguish
between good and bad parameters, a high ``min_resources`` is recommended. On
the other hand if the distinction is clear even with a small amount of
samples, then a small ``min_resources`` may be preferable since it would
speed up the computation.

Notice in the example above that the last iteration does not use the maximum
amount of resources available: 1000 samples are available, yet only 640 are
used, at most. By default, both :class:`HalvingRandomSearchCV` and
:class:`HalvingGridSearchCV` try to use as many resources as possible in the
last iteration, with the constraint that this amount of resources must be a
multiple of both `min_resources` and `factor` (this constraint will be clear
in the next section). :class:`HalvingRandomSearchCV` achieves this by
sampling the right amount of candidates, while :class:`HalvingGridSearchCV`
achieves this by properly setting `min_resources`. Please see
:ref:`exhausting_the_resources` for details.

.. _amount_of_resource_and_number_of_candidates:

Amount of resource and number of candidates at each iteration
-------------------------------------------------------------

At any iteration `i`, each candidate is allocated a given amount of resources
which we denote `n_resources_i`. This quantity is controlled by the
parameters ``factor`` and ``min_resources`` as follows (`factor` is strictly
greater than 1)::

    n_resources_i = factor**i * min_resources,

or equivalently::

    n_resources_{i+1} = n_resources_i * factor

where ``min_resources == n_resources_0`` is the amount of resources used at
the first iteration. ``factor`` also defines the proportions of candidates
that will be selected for the next iteration::

    n_candidates_i = n_candidates // (factor ** i)

or equivalently::

    n_candidates_0 = n_candidates
    n_candidates_{i+1} = n_candidates_i // factor

So in the first iteration, we use ``min_resources`` resources
``n_candidates`` times. In the second iteration, we use ``min_resources *
factor`` resources ``n_candidates // factor`` times. The third again
multiplies the resources per candidate and divides the number of candidates.
This process stops when the maximum amount of resource per candidate is
reached, or when we have identified the best candidate. The best candidate
is identified at the iteration that is evaluating `factor` or less candidates
(see just below for an explanation).

Here is an example with ``min_resources=3`` and ``factor=2``, starting with
70 candidates:

+-----------------------+-----------------------+
| ``n_resources_i``     | ``n_candidates_i``    |
+=======================+=======================+
| 3 (=min_resources)    | 70 (=n_candidates)    |
+-----------------------+-----------------------+
| 3 * 2 = 6             | 70 // 2 = 35          |
+-----------------------+-----------------------+
| 6 * 2 = 12            | 35 // 2 = 17          |
+-----------------------+-----------------------+
| 12 * 2 = 24           | 17 // 2 = 8           |
+-----------------------+-----------------------+
| 24 * 2 = 48           | 8 // 2 = 4            |
+-----------------------+-----------------------+
| 48 * 2 = 96           | 4 // 2 = 2            |
+-----------------------+-----------------------+

We can note that:

- the process stops at the first iteration which evaluates `factor=2`
  candidates: the best candidate is the best out of these 2 candidates. It
  is not necessary to run an additional iteration, since it would only
  evaluate one candidate (namely the best one, which we have already
  identified). For this reason, in general, we want the last iteration to
  run at most ``factor`` candidates. If the last iteration evaluates more
  than `factor` candidates, then this last iteration reduces to a regular
  search (as in :class:`RandomizedSearchCV` or :class:`GridSearchCV`).
- each ``n_resources_i`` is a multiple of both ``factor`` and
  ``min_resources`` (which is confirmed by its definition above).

The amount of resources that is used at each iteration can be found in the
`n_resources_` attribute.

Choosing a resource
-------------------

By default, the resource is defined in terms of number of samples. That is,
each iteration will use an increasing amount of samples to train on. You can
however manually specify a parameter to use as the resource with the
``resource`` parameter. Here is an example where the resource is defined in
terms of the number of estimators of a random forest::

    >>> from sklearn.datasets import make_classification
    >>> from sklearn.ensemble import RandomForestClassifier
    >>> from sklearn.experimental import enable_halving_search_cv  # noqa
    >>> from sklearn.model_selection import HalvingGridSearchCV
    >>> import pandas as pd
    >>>
    >>> param_grid = {'max_depth': [3, 5, 10],
    ...               'min_samples_split': [2, 5, 10]}
    >>> base_estimator = RandomForestClassifier(random_state=0)
    >>> X, y = make_classification(n_samples=1000, random_state=0)
    >>> sh = HalvingGridSearchCV(base_estimator, param_grid, cv=5,
    ...                          factor=2, resource='n_estimators',
    ...                          max_resources=30).fit(X, y)
    >>> sh.best_estimator_
    RandomForestClassifier(max_depth=5, n_estimators=24, random_state=0)

Note that it is not possible to budget on a parameter that is part of the
parameter grid.

.. _exhausting_the_resources:

Exhausting the available resources
----------------------------------

As mentioned above, the number of resources that is used at each iteration
depends on the `min_resources` parameter.
If you have a lot of resources available but start with a low number of
resources, some of them might be wasted (i.e. not used)::

    >>> from sklearn.datasets import make_classification
    >>> from sklearn.svm import SVC
    >>> from sklearn.experimental import enable_halving_search_cv  # noqa
    >>> from sklearn.model_selection import HalvingGridSearchCV
    >>> import pandas as pd
    >>> param_grid= {'kernel': ('linear', 'rbf'),
    ...              'C': [1, 10, 100]}
    >>> base_estimator = SVC(gamma='scale')
    >>> X, y = make_classification(n_samples=1000)
    >>> sh = HalvingGridSearchCV(base_estimator, param_grid, cv=5,
    ...                          factor=2, min_resources=20).fit(X, y)
    >>> sh.n_resources_
    [20, 40, 80]

The search process will only use 80 resources at most, while our maximum
amount of available resources is ``n_samples=1000``. Here, we have
``min_resources = r_0 = 20``.

For :class:`HalvingGridSearchCV`, by default, the `min_resources` parameter
is set to 'exhaust'. This means that `min_resources` is automatically set
such that the last iteration can use as many resources as possible, within
the `max_resources` limit::

    >>> sh = HalvingGridSearchCV(base_estimator, param_grid, cv=5,
    ...                          factor=2, min_resources='exhaust').fit(X, y)
    >>> sh.n_resources_
    [250, 500, 1000]

`min_resources` was here automatically set to 250, which results in the last
iteration using all the resources. The exact value that is used depends on
the number of candidate parameter, on `max_resources` and on `factor`.

For :class:`HalvingRandomSearchCV`, exhausting the resources can be done in 2
ways:

- by setting `min_resources='exhaust'`, just like for
  :class:`HalvingGridSearchCV`;
- by setting `n_candidates='exhaust'`.

Both options are mutually exclusive: using `min_resources='exhaust'` requires
knowing the number of candidates, and symmetrically `n_candidates='exhaust'`
requires knowing `min_resources`.

In general, exhausting the total number of resources leads to a better final
candidate parameter, and is slightly more time-intensive.

.. _aggressive_elimination:

Aggressive elimination of candidates
------------------------------------

Ideally, we want the last iteration to evaluate ``factor`` candidates (see
:ref:`amount_of_resource_and_number_of_candidates`). We then just have to
pick the best one. When the number of available resources is small with
respect to the number of candidates, the last iteration may have to evaluate
more than ``factor`` candidates::

    >>> from sklearn.datasets import make_classification
    >>> from sklearn.svm import SVC
    >>> from sklearn.experimental import enable_halving_search_cv  # noqa
    >>> from sklearn.model_selection import HalvingGridSearchCV
    >>> import pandas as pd
    >>>
    >>>
    >>> param_grid = {'kernel': ('linear', 'rbf'),
    ...               'C': [1, 10, 100]}
    >>> base_estimator = SVC(gamma='scale')
    >>> X, y = make_classification(n_samples=1000)
    >>> sh = HalvingGridSearchCV(base_estimator, param_grid, cv=5,
    ...                          factor=2, max_resources=40,
    ...                          aggressive_elimination=False).fit(X, y)
    >>> sh.n_resources_
    [20, 40]
    >>> sh.n_candidates_
    [6, 3]

Since we cannot use more than ``max_resources=40`` resources, the process
has to stop at the second iteration which evaluates more than ``factor=2``
candidates.

Using the ``aggressive_elimination`` parameter, you can force the search
process to end up with less than ``factor`` candidates at the last
iteration. To do this, the process will eliminate as many candidates as
necessary using ``min_resources`` resources::

    >>> sh = HalvingGridSearchCV(base_estimator, param_grid, cv=5,
    ...                            factor=2,
    ...                            max_resources=40,
    ...                            aggressive_elimination=True,
    ...                            ).fit(X, y)
    >>> sh.n_resources_
    [20, 20,  40]
    >>> sh.n_candidates_
    [6, 3, 2]

Notice that we end with 2 candidates at the last iteration since we have
eliminated enough candidates during the first iterations, using ``n_resources =
min_resources = 20``.

.. _successive_halving_cv_results:

Analyzing results with the `cv_results_` attribute
--------------------------------------------------

The ``cv_results_`` attribute contains useful information for analyzing the
results of a search. It can be converted to a pandas dataframe with ``df =
pd.DataFrame(est.cv_results_)``. The ``cv_results_`` attribute of
:class:`HalvingGridSearchCV` and :class:`HalvingRandomSearchCV` is similar
to that of :class:`GridSearchCV` and :class:`RandomizedSearchCV`, with
additional information related to the successive halving process.

Here is an example with some of the columns of a (truncated) dataframe:

====  ======  ===============  =================  ========================================================================================
  ..    iter      n_resources    mean_test_score  params
====  ======  ===============  =================  ========================================================================================
   0       0              125           0.983667  {'criterion': 'log_loss', 'max_depth': None, 'max_features': 9, 'min_samples_split': 5}
   1       0              125           0.983667  {'criterion': 'gini', 'max_depth': None, 'max_features': 8, 'min_samples_split': 7}
   2       0              125           0.983667  {'criterion': 'gini', 'max_depth': None, 'max_features': 10, 'min_samples_split': 10}
   3       0              125           0.983667  {'criterion': 'log_loss', 'max_depth': None, 'max_features': 6, 'min_samples_split': 6}
 ...     ...              ...                ...  ...
  15       2              500           0.951958  {'criterion': 'log_loss', 'max_depth': None, 'max_features': 9, 'min_samples_split': 10}
  16       2              500           0.947958  {'criterion': 'gini', 'max_depth': None, 'max_features': 10, 'min_samples_split': 10}
  17       2              500           0.951958  {'criterion': 'gini', 'max_depth': None, 'max_features': 10, 'min_samples_split': 4}
  18       3             1000           0.961009  {'criterion': 'log_loss', 'max_depth': None, 'max_features': 9, 'min_samples_split': 10}
  19       3             1000           0.955989  {'criterion': 'gini', 'max_depth': None, 'max_features': 10, 'min_samples_split': 4}
====  ======  ===============  =================  ========================================================================================

Each row corresponds to a given parameter combination (a candidate) and a given
iteration. The iteration is given by the ``iter`` column. The ``n_resources``
column tells you how many resources were used.

In the example above, the best parameter combination is ``{'criterion':
'log_loss', 'max_depth': None, 'max_features': 9, 'min_samples_split': 10}``
since it has reached the last iteration (3) with the highest score:
0.96.

.. topic:: References:

    .. [1] K. Jamieson, A. Talwalkar,
       `Non-stochastic Best Arm Identification and Hyperparameter
       Optimization <http://proceedings.mlr.press/v51/jamieson16.html>`_, in
       proc. of Machine Learning Research, 2016.
    .. [2] L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, A. Talwalkar,
       :arxiv:`Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
       <1603.06560>`, in Machine Learning Research 18, 2018.

.. _grid_search_tips:

Tips for parameter search
=========================

.. _gridsearch_scoring:

Specifying an objective metric
------------------------------

By default, parameter search uses the ``score`` function of the estimator
to evaluate a parameter setting. These are the
:func:`sklearn.metrics.accuracy_score` for classification and
:func:`sklearn.metrics.r2_score` for regression.  For some applications,
other scoring functions are better suited (for example in unbalanced
classification, the accuracy score is often uninformative). An alternative
scoring function can be specified via the ``scoring`` parameter of most
parameter search tools. See :ref:`scoring_parameter` for more details.

.. _multimetric_grid_search:

Specifying multiple metrics for evaluation
------------------------------------------

:class:`GridSearchCV` and :class:`RandomizedSearchCV` allow specifying
multiple metrics for the ``scoring`` parameter.

Multimetric scoring can either be specified as a list of strings of predefined
scores names or a dict mapping the scorer name to the scorer function and/or
the predefined scorer name(s). See :ref:`multimetric_scoring` for more details.

When specifying multiple metrics, the ``refit`` parameter must be set to the
metric (string) for which the ``best_params_`` will be found and used to build
the ``best_estimator_`` on the whole dataset. If the search should not be
refit, set ``refit=False``. Leaving refit to the default value ``None`` will
result in an error when using multiple metrics.

See :ref:`sphx_glr_auto_examples_model_selection_plot_multi_metric_evaluation.py`
for an example usage.

:class:`HalvingRandomSearchCV` and :class:`HalvingGridSearchCV` do not support
multimetric scoring.

.. _composite_grid_search:

Composite estimators and parameter spaces
-----------------------------------------
:class:`GridSearchCV` and :class:`RandomizedSearchCV` allow searching over
parameters of composite or nested estimators such as
:class:`~sklearn.pipeline.Pipeline`,
:class:`~sklearn.compose.ColumnTransformer`,
:class:`~sklearn.ensemble.VotingClassifier` or
:class:`~sklearn.calibration.CalibratedClassifierCV` using a dedicated
``<estimator>__<parameter>`` syntax::

  >>> from sklearn.model_selection import GridSearchCV
  >>> from sklearn.calibration import CalibratedClassifierCV
  >>> from sklearn.ensemble import RandomForestClassifier
  >>> from sklearn.datasets import make_moons
  >>> X, y = make_moons()
  >>> calibrated_forest = CalibratedClassifierCV(
  ...    estimator=RandomForestClassifier(n_estimators=10))
  >>> param_grid = {
  ...    'estimator__max_depth': [2, 4, 6, 8]}
  >>> search = GridSearchCV(calibrated_forest, param_grid, cv=5)
  >>> search.fit(X, y)
  GridSearchCV(cv=5,
               estimator=CalibratedClassifierCV(...),
               param_grid={'estimator__max_depth': [2, 4, 6, 8]})

Here, ``<estimator>`` is the parameter name of the nested estimator,
in this case ``estimator``.
If the meta-estimator is constructed as a collection of estimators as in
`pipeline.Pipeline`, then ``<estimator>`` refers to the name of the estimator,
see :ref:`pipeline_nested_parameters`. In practice, there can be several
levels of nesting::

  >>> from sklearn.pipeline import Pipeline
  >>> from sklearn.feature_selection import SelectKBest
  >>> pipe = Pipeline([
  ...    ('select', SelectKBest()),
  ...    ('model', calibrated_forest)])
  >>> param_grid = {
  ...    'select__k': [1, 2],
  ...    'model__estimator__max_depth': [2, 4, 6, 8]}
  >>> search = GridSearchCV(pipe, param_grid, cv=5).fit(X, y)

Please refer to :ref:`pipeline` for performing parameter searches over
pipelines.

Model selection: development and evaluation
-------------------------------------------

Model selection by evaluating various parameter settings can be seen as a way
to use the labeled data to "train" the parameters of the grid.

When evaluating the resulting model it is important to do it on
held-out samples that were not seen during the grid search process:
it is recommended to split the data into a **development set** (to
be fed to the :class:`GridSearchCV` instance) and an **evaluation set**
to compute performance metrics.

This can be done by using the :func:`train_test_split`
utility function.

Parallelism
-----------

The parameter search tools evaluate each parameter combination on each data
fold independently. Computations can be run in parallel by using the keyword
``n_jobs=-1``. See function signature for more details, and also the Glossary
entry for :term:`n_jobs`.

Robustness to failure
---------------------

Some parameter settings may result in a failure to ``fit`` one or more folds
of the data.  By default, this will cause the entire search to fail, even if
some parameter settings could be fully evaluated. Setting ``error_score=0``
(or `=np.nan`) will make the procedure robust to such failure, issuing a
warning and setting the score for that fold to 0 (or `nan`), but completing
the search.

.. _alternative_cv:

Alternatives to brute force parameter search
============================================

Model specific cross-validation
-------------------------------


Some models can fit data for a range of values of some parameter almost
as efficiently as fitting the estimator for a single value of the
parameter. This feature can be leveraged to perform a more efficient
cross-validation used for model selection of this parameter.

The most common parameter amenable to this strategy is the parameter
encoding the strength of the regularizer. In this case we say that we
compute the **regularization path** of the estimator.

Here is the list of such models:

.. currentmodule:: sklearn

.. autosummary::

   linear_model.ElasticNetCV
   linear_model.LarsCV
   linear_model.LassoCV
   linear_model.LassoLarsCV
   linear_model.LogisticRegressionCV
   linear_model.MultiTaskElasticNetCV
   linear_model.MultiTaskLassoCV
   linear_model.OrthogonalMatchingPursuitCV
   linear_model.RidgeCV
   linear_model.RidgeClassifierCV


Information Criterion
---------------------

Some models can offer an information-theoretic closed-form formula of the
optimal estimate of the regularization parameter by computing a single
regularization path (instead of several when using cross-validation).

Here is the list of models benefiting from the Akaike Information
Criterion (AIC) or the Bayesian Information Criterion (BIC) for automated
model selection:

.. autosummary::

   linear_model.LassoLarsIC


.. _out_of_bag:

Out of Bag Estimates
--------------------

When using ensemble methods base upon bagging, i.e. generating new
training sets using sampling with replacement, part of the training set
remains unused.  For each classifier in the ensemble, a different part
of the training set is left out.

This left out portion can be used to estimate the generalization error
without having to rely on a separate validation set.  This estimate
comes "for free" as no additional data is needed and can be used for
model selection.

This is currently implemented in the following classes:

.. autosummary::

    ensemble.RandomForestClassifier
    ensemble.RandomForestRegressor
    ensemble.ExtraTreesClassifier
    ensemble.ExtraTreesRegressor
    ensemble.GradientBoostingClassifier
    ensemble.GradientBoostingRegressor