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<section id="halvinggridsearchcv">
<h1>HalvingGridSearchCV<a class="headerlink" href="#halvinggridsearchcv" title="Link to this heading">#</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">HalvingGridSearchCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'n_samples'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_resources</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_resources</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'exhaust'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aggressive_elimination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_train_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/model_selection/_search_successive_halving.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV" title="Link to this definition">#</a></dt>
<dd><p>Search over specified parameter values with successive halving.</p>
<p>The search strategy starts evaluating all the candidates with a small
amount of resources and iteratively selects the best candidates, using
more and more resources.</p>
<p>Read more in the <a class="reference internal" href="../grid_search.html#successive-halving-user-guide"><span class="std std-ref">User guide</span></a>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This estimator is still <strong>experimental</strong> for now: the predictions
and the API might change without any deprecation cycle. To use it,
you need to explicitly import <code class="docutils literal notranslate"><span class="pre">enable_halving_search_cv</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># explicitly require this experimental feature</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.experimental</span> <span class="kn">import</span> <span class="n">enable_halving_search_cv</span> <span class="c1"># noqa</span>
<span class="gp">>>> </span><span class="c1"># now you can import normally from model_selection</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">HalvingGridSearchCV</span>
</pre></div>
</div>
</div>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>estimator</strong><span class="classifier">estimator object</span></dt><dd><p>This is assumed to implement the scikit-learn estimator interface.
Either estimator needs to provide a <code class="docutils literal notranslate"><span class="pre">score</span></code> function,
or <code class="docutils literal notranslate"><span class="pre">scoring</span></code> must be passed.</p>
</dd>
<dt><strong>param_grid</strong><span class="classifier">dict or list of dictionaries</span></dt><dd><p>Dictionary with parameters names (string) as keys and lists of
parameter settings to try as values, or a list of such
dictionaries, in which case the grids spanned by each dictionary
in the list are explored. This enables searching over any sequence
of parameter settings.</p>
</dd>
<dt><strong>factor</strong><span class="classifier">int or float, default=3</span></dt><dd><p>The ‘halving’ parameter, which determines the proportion of candidates
that are selected for each subsequent iteration. For example,
<code class="docutils literal notranslate"><span class="pre">factor=3</span></code> means that only one third of the candidates are selected.</p>
</dd>
<dt><strong>resource</strong><span class="classifier"><code class="docutils literal notranslate"><span class="pre">'n_samples'</span></code> or str, default=’n_samples’</span></dt><dd><p>Defines the resource that increases with each iteration. By default,
the resource is the number of samples. It can also be set to any
parameter of the base estimator that accepts positive integer
values, e.g. ‘n_iterations’ or ‘n_estimators’ for a gradient
boosting estimator. In this case <code class="docutils literal notranslate"><span class="pre">max_resources</span></code> cannot be ‘auto’
and must be set explicitly.</p>
</dd>
<dt><strong>max_resources</strong><span class="classifier">int, default=’auto’</span></dt><dd><p>The maximum amount of resource that any candidate is allowed to use
for a given iteration. By default, this is set to <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> when
<code class="docutils literal notranslate"><span class="pre">resource='n_samples'</span></code> (default), else an error is raised.</p>
</dd>
<dt><strong>min_resources</strong><span class="classifier">{‘exhaust’, ‘smallest’} or int, default=’exhaust’</span></dt><dd><p>The minimum amount of resource that any candidate is allowed to use
for a given iteration. Equivalently, this defines the amount of
resources <code class="docutils literal notranslate"><span class="pre">r0</span></code> that are allocated for each candidate at the first
iteration.</p>
<ul class="simple">
<li><p>‘smallest’ is a heuristic that sets <code class="docutils literal notranslate"><span class="pre">r0</span></code> to a small value:</p>
<ul>
<li><p><code class="docutils literal notranslate"><span class="pre">n_splits</span> <span class="pre">*</span> <span class="pre">2</span></code> when <code class="docutils literal notranslate"><span class="pre">resource='n_samples'</span></code> for a regression problem</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">n_classes</span> <span class="pre">*</span> <span class="pre">n_splits</span> <span class="pre">*</span> <span class="pre">2</span></code> when <code class="docutils literal notranslate"><span class="pre">resource='n_samples'</span></code> for a
classification problem</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">1</span></code> when <code class="docutils literal notranslate"><span class="pre">resource</span> <span class="pre">!=</span> <span class="pre">'n_samples'</span></code></p></li>
</ul>
</li>
<li><p>‘exhaust’ will set <code class="docutils literal notranslate"><span class="pre">r0</span></code> such that the <strong>last</strong> iteration uses as
much resources as possible. Namely, the last iteration will use the
highest value smaller than <code class="docutils literal notranslate"><span class="pre">max_resources</span></code> that is a multiple of
both <code class="docutils literal notranslate"><span class="pre">min_resources</span></code> and <code class="docutils literal notranslate"><span class="pre">factor</span></code>. In general, using ‘exhaust’
leads to a more accurate estimator, but is slightly more time
consuming.</p></li>
</ul>
<p>Note that the amount of resources used at each iteration is always a
multiple of <code class="docutils literal notranslate"><span class="pre">min_resources</span></code>.</p>
</dd>
<dt><strong>aggressive_elimination</strong><span class="classifier">bool, default=False</span></dt><dd><p>This is only relevant in cases where there isn’t enough resources to
reduce the remaining candidates to at most <code class="docutils literal notranslate"><span class="pre">factor</span></code> after the last
iteration. If <code class="docutils literal notranslate"><span class="pre">True</span></code>, then the search process will ‘replay’ the
first iteration for as long as needed until the number of candidates
is small enough. This is <code class="docutils literal notranslate"><span class="pre">False</span></code> by default, which means that the
last iteration may evaluate more than <code class="docutils literal notranslate"><span class="pre">factor</span></code> candidates. See
<a class="reference internal" href="../grid_search.html#aggressive-elimination"><span class="std std-ref">Aggressive elimination of candidates</span></a> for more details.</p>
</dd>
<dt><strong>cv</strong><span class="classifier">int, cross-validation generator or iterable, default=5</span></dt><dd><p>Determines the cross-validation splitting strategy.
Possible inputs for cv are:</p>
<ul class="simple">
<li><p>integer, to specify the number of folds in a <code class="docutils literal notranslate"><span class="pre">(Stratified)KFold</span></code>,</p></li>
<li><p><a class="reference internal" href="../../glossary.html#term-CV-splitter"><span class="xref std std-term">CV splitter</span></a>,</p></li>
<li><p>An iterable yielding (train, test) splits as arrays of indices.</p></li>
</ul>
<p>For integer/None inputs, if the estimator is a classifier and <code class="docutils literal notranslate"><span class="pre">y</span></code> is
either binary or multiclass, <a class="reference internal" href="sklearn.model_selection.StratifiedKFold.html#sklearn.model_selection.StratifiedKFold" title="sklearn.model_selection.StratifiedKFold"><code class="xref py py-class docutils literal notranslate"><span class="pre">StratifiedKFold</span></code></a> is used. In all
other cases, <a class="reference internal" href="sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold"><code class="xref py py-class docutils literal notranslate"><span class="pre">KFold</span></code></a> is used. These splitters are instantiated
with <code class="docutils literal notranslate"><span class="pre">shuffle=False</span></code> so the splits will be the same across calls.</p>
<p>Refer <a class="reference internal" href="../cross_validation.html#cross-validation"><span class="std std-ref">User Guide</span></a> for the various
cross-validation strategies that can be used here.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Due to implementation details, the folds produced by <code class="docutils literal notranslate"><span class="pre">cv</span></code> must be
the same across multiple calls to <code class="docutils literal notranslate"><span class="pre">cv.split()</span></code>. For
built-in <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> iterators, this can be achieved by
deactivating shuffling (<code class="docutils literal notranslate"><span class="pre">shuffle=False</span></code>), or by setting the
<code class="docutils literal notranslate"><span class="pre">cv</span></code>’s <code class="docutils literal notranslate"><span class="pre">random_state</span></code> parameter to an integer.</p>
</div>
</dd>
<dt><strong>scoring</strong><span class="classifier">str, callable, or None, default=None</span></dt><dd><p>A single string (see <a class="reference internal" href="../model_evaluation.html#scoring-parameter"><span class="std std-ref">The scoring parameter: defining model evaluation rules</span></a>) or a callable
(see <a class="reference internal" href="../model_evaluation.html#scoring"><span class="std std-ref">Defining your scoring strategy from metric functions</span></a>) to evaluate the predictions on the test set.
If None, the estimator’s score method is used.</p>
</dd>
<dt><strong>refit</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, refit an estimator using the best found parameters on the
whole dataset.</p>
<p>The refitted estimator is made available at the <code class="docutils literal notranslate"><span class="pre">best_estimator_</span></code>
attribute and permits using <code class="docutils literal notranslate"><span class="pre">predict</span></code> directly on this
<code class="docutils literal notranslate"><span class="pre">HalvingGridSearchCV</span></code> instance.</p>
</dd>
<dt><strong>error_score</strong><span class="classifier">‘raise’ or numeric</span></dt><dd><p>Value to assign to the score if an error occurs in estimator fitting.
If set to ‘raise’, the error is raised. If a numeric value is given,
FitFailedWarning is raised. This parameter does not affect the refit
step, which will always raise the error. Default is <code class="docutils literal notranslate"><span class="pre">np.nan</span></code>.</p>
</dd>
<dt><strong>return_train_score</strong><span class="classifier">bool, default=False</span></dt><dd><p>If <code class="docutils literal notranslate"><span class="pre">False</span></code>, the <code class="docutils literal notranslate"><span class="pre">cv_results_</span></code> attribute will not include training
scores.
Computing training scores is used to get insights on how different
parameter settings impact the overfitting/underfitting trade-off.
However computing the scores on the training set can be computationally
expensive and is not strictly required to select the parameters that
yield the best generalization performance.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=None</span></dt><dd><p>Pseudo random number generator state used for subsampling the dataset
when <code class="docutils literal notranslate"><span class="pre">resources</span> <span class="pre">!=</span> <span class="pre">'n_samples'</span></code>. Ignored otherwise.
Pass an int for reproducible output across multiple function calls.
See <a class="reference internal" href="../../glossary.html#term-random_state"><span class="xref std std-term">Glossary</span></a>.</p>
</dd>
<dt><strong>n_jobs</strong><span class="classifier">int or None, default=None</span></dt><dd><p>Number of jobs to run in parallel.
<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="../../glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
for more details.</p>
</dd>
<dt><strong>verbose</strong><span class="classifier">int</span></dt><dd><p>Controls the verbosity: the higher, the more messages.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>n_resources_</strong><span class="classifier">list of int</span></dt><dd><p>The amount of resources used at each iteration.</p>
</dd>
<dt><strong>n_candidates_</strong><span class="classifier">list of int</span></dt><dd><p>The number of candidate parameters that were evaluated at each
iteration.</p>
</dd>
<dt><strong>n_remaining_candidates_</strong><span class="classifier">int</span></dt><dd><p>The number of candidate parameters that are left after the last
iteration. It corresponds to <code class="docutils literal notranslate"><span class="pre">ceil(n_candidates[-1]</span> <span class="pre">/</span> <span class="pre">factor)</span></code></p>
</dd>
<dt><strong>max_resources_</strong><span class="classifier">int</span></dt><dd><p>The maximum number of resources that any candidate is allowed to use
for a given iteration. Note that since the number of resources used
at each iteration must be a multiple of <code class="docutils literal notranslate"><span class="pre">min_resources_</span></code>, the
actual number of resources used at the last iteration may be smaller
than <code class="docutils literal notranslate"><span class="pre">max_resources_</span></code>.</p>
</dd>
<dt><strong>min_resources_</strong><span class="classifier">int</span></dt><dd><p>The amount of resources that are allocated for each candidate at the
first iteration.</p>
</dd>
<dt><strong>n_iterations_</strong><span class="classifier">int</span></dt><dd><p>The actual number of iterations that were run. This is equal to
<code class="docutils literal notranslate"><span class="pre">n_required_iterations_</span></code> if <code class="docutils literal notranslate"><span class="pre">aggressive_elimination</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>.
Else, this is equal to <code class="docutils literal notranslate"><span class="pre">min(n_possible_iterations_,</span>
<span class="pre">n_required_iterations_)</span></code>.</p>
</dd>
<dt><strong>n_possible_iterations_</strong><span class="classifier">int</span></dt><dd><p>The number of iterations that are possible starting with
<code class="docutils literal notranslate"><span class="pre">min_resources_</span></code> resources and without exceeding
<code class="docutils literal notranslate"><span class="pre">max_resources_</span></code>.</p>
</dd>
<dt><strong>n_required_iterations_</strong><span class="classifier">int</span></dt><dd><p>The number of iterations that are required to end up with less than
<code class="docutils literal notranslate"><span class="pre">factor</span></code> candidates at the last iteration, starting with
<code class="docutils literal notranslate"><span class="pre">min_resources_</span></code> resources. This will be smaller than
<code class="docutils literal notranslate"><span class="pre">n_possible_iterations_</span></code> when there isn’t enough resources.</p>
</dd>
<dt><strong>cv_results_</strong><span class="classifier">dict of numpy (masked) ndarrays</span></dt><dd><p>A dict with keys as column headers and values as columns, that can be
imported into a pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>. It contains lots of information
for analysing the results of a search.
Please refer to the <a class="reference internal" href="../grid_search.html#successive-halving-cv-results"><span class="std std-ref">User guide</span></a>
for details.</p>
</dd>
<dt><strong>best_estimator_</strong><span class="classifier">estimator or dict</span></dt><dd><p>Estimator that was chosen by the search, i.e. estimator
which gave highest score (or smallest loss if specified)
on the left out data. Not available if <code class="docutils literal notranslate"><span class="pre">refit=False</span></code>.</p>
</dd>
<dt><strong>best_score_</strong><span class="classifier">float</span></dt><dd><p>Mean cross-validated score of the best_estimator.</p>
</dd>
<dt><strong>best_params_</strong><span class="classifier">dict</span></dt><dd><p>Parameter setting that gave the best results on the hold out data.</p>
</dd>
<dt><strong>best_index_</strong><span class="classifier">int</span></dt><dd><p>The index (of the <code class="docutils literal notranslate"><span class="pre">cv_results_</span></code> arrays) which corresponds to the best
candidate parameter setting.</p>
<p>The dict at <code class="docutils literal notranslate"><span class="pre">search.cv_results_['params'][search.best_index_]</span></code> gives
the parameter setting for the best model, that gives the highest
mean score (<code class="docutils literal notranslate"><span class="pre">search.best_score_</span></code>).</p>
</dd>
<dt><strong>scorer_</strong><span class="classifier">function or a dict</span></dt><dd><p>Scorer function used on the held out data to choose the best
parameters for the model.</p>
</dd>
<dt><strong>n_splits_</strong><span class="classifier">int</span></dt><dd><p>The number of cross-validation splits (folds/iterations).</p>
</dd>
<dt><strong>refit_time_</strong><span class="classifier">float</span></dt><dd><p>Seconds used for refitting the best model on the whole dataset.</p>
<p>This is present only if <code class="docutils literal notranslate"><span class="pre">refit</span></code> is not False.</p>
</dd>
<dt><strong>multimetric_</strong><span class="classifier">bool</span></dt><dd><p>Whether or not the scorers compute several metrics.</p>
</dd>
<dt><a class="reference internal" href="#sklearn.model_selection.HalvingGridSearchCV.classes_" title="sklearn.model_selection.HalvingGridSearchCV.classes_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">classes_</span></code></a><span class="classifier">ndarray of shape (n_classes,)</span></dt><dd><p>Class labels.</p>
</dd>
<dt><a class="reference internal" href="#sklearn.model_selection.HalvingGridSearchCV.n_features_in_" title="sklearn.model_selection.HalvingGridSearchCV.n_features_in_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">n_features_in_</span></code></a><span class="classifier">int</span></dt><dd><p>Number of features seen during <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a>.</p>
</dd>
<dt><strong>feature_names_in_</strong><span class="classifier">ndarray of shape (<code class="docutils literal notranslate"><span class="pre">n_features_in_</span></code>,)</span></dt><dd><p>Names of features seen during <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a>. Only defined if
<code class="docutils literal notranslate"><span class="pre">best_estimator_</span></code> is defined (see the documentation for the <code class="docutils literal notranslate"><span class="pre">refit</span></code>
parameter for more details) and that <code class="docutils literal notranslate"><span class="pre">best_estimator_</span></code> exposes
<code class="docutils literal notranslate"><span class="pre">feature_names_in_</span></code> when fit.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.0.</span></p>
</div>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.model_selection.HalvingRandomSearchCV.html#sklearn.model_selection.HalvingRandomSearchCV" title="sklearn.model_selection.HalvingRandomSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">HalvingRandomSearchCV</span></code></a></dt><dd><p>Random search over a set of parameters using successive halving.</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>The parameters selected are those that maximize the score of the held-out
data, according to the scoring parameter.</p>
<p>All parameter combinations scored with a NaN will share the lowest rank.</p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_iris</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.experimental</span> <span class="kn">import</span> <span class="n">enable_halving_search_cv</span> <span class="c1"># noqa</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">HalvingGridSearchCV</span>
<span class="gp">...</span>
<span class="gp">>>> </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">load_iris</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">clf</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">...</span>
<span class="gp">>>> </span><span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"max_depth"</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span>
<span class="gp">... </span> <span class="s2">"min_samples_split"</span><span class="p">:</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">]}</span>
<span class="gp">>>> </span><span class="n">search</span> <span class="o">=</span> <span class="n">HalvingGridSearchCV</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">resource</span><span class="o">=</span><span class="s1">'n_estimators'</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">max_resources</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">search</span><span class="o">.</span><span class="n">best_params_</span>
<span class="go">{'max_depth': None, 'min_samples_split': 10, 'n_estimators': 9}</span>
</pre></div>
</div>
<dl class="py property">
<dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV.classes_">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">classes_</span></span><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.classes_" title="Link to this definition">#</a></dt>
<dd><p>Class labels.</p>
<p>Only available when <code class="docutils literal notranslate"><span class="pre">refit=True</span></code> and the estimator is a classifier.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV.decision_function">
<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/model_selection/_search.py#L643"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.decision_function" title="Link to this definition">#</a></dt>
<dd><p>Call decision_function on the estimator with the best found parameters.</p>
<p>Only available if <code class="docutils literal notranslate"><span class="pre">refit=True</span></code> and the underlying estimator supports
<code class="docutils literal notranslate"><span class="pre">decision_function</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">indexable, length n_samples</span></dt><dd><p>Must fulfill the input assumptions of the
underlying estimator.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y_score</strong><span class="classifier">ndarray of shape (n_samples,) or (n_samples, n_classes) or (n_samples, n_classes * (n_classes-1) / 2)</span></dt><dd><p>Result of the decision function for <code class="docutils literal notranslate"><span class="pre">X</span></code> based on the estimator with
the best found parameters.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV.fit">
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/model_selection/_search_successive_halving.py#L216"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.fit" title="Link to this definition">#</a></dt>
<dd><p>Run fit with all sets of parameters.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_samples, n_features)</span></dt><dd><p>Training vector, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
<code class="docutils literal notranslate"><span class="pre">n_features</span></code> is the number of features.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like, shape (n_samples,) or (n_samples, n_output), optional</span></dt><dd><p>Target relative to X for classification or regression;
None for unsupervised learning.</p>
</dd>
<dt><strong>**params</strong><span class="classifier">dict of string -> object</span></dt><dd><p>Parameters passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method of the estimator.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Instance of fitted estimator.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV.get_metadata_routing">
<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/model_selection/_search.py#L1165"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.get_metadata_routing" title="Link to this definition">#</a></dt>
<dd><p>Get metadata routing of this object.</p>
<p>Please check <a class="reference internal" href="../../metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
mechanism works.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.4.</span></p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>routing</strong><span class="classifier">MetadataRouter</span></dt><dd><p>A <a class="reference internal" href="sklearn.utils.metadata_routing.MetadataRouter.html#sklearn.utils.metadata_routing.MetadataRouter" title="sklearn.utils.metadata_routing.MetadataRouter"><code class="xref py py-class docutils literal notranslate"><span class="pre">MetadataRouter</span></code></a> encapsulating
routing information.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV.get_params">
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/base.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.get_params" title="Link to this definition">#</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">dict</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV.inverse_transform">
<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xt</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/model_selection/_search.py#L688"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.inverse_transform" title="Link to this definition">#</a></dt>
<dd><p>Call inverse_transform on the estimator with the best found params.</p>
<p>Only available if the underlying estimator implements
<code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> and <code class="docutils literal notranslate"><span class="pre">refit=True</span></code>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>X</strong><span class="classifier">indexable, length n_samples</span></dt><dd><p>Must fulfill the input assumptions of the
underlying estimator.</p>
</dd>
<dt><strong>Xt</strong><span class="classifier">indexable, length n_samples</span></dt><dd><p>Must fulfill the input assumptions of the
underlying estimator.</p>
<div class="deprecated">
<p><span class="versionmodified deprecated">Deprecated since version 1.5: </span><code class="docutils literal notranslate"><span class="pre">Xt</span></code> was deprecated in 1.5 and will be removed in 1.7. Use <code class="docutils literal notranslate"><span class="pre">X</span></code> instead.</p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Result of the <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> function for <code class="docutils literal notranslate"><span class="pre">Xt</span></code> based on the
estimator with the best found parameters.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV.n_features_in_">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">n_features_in_</span></span><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.n_features_in_" title="Link to this definition">#</a></dt>
<dd><p>Number of features seen during <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a>.</p>
<p>Only available when <code class="docutils literal notranslate"><span class="pre">refit=True</span></code>.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV.predict">
<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/model_selection/_search.py#L575"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.predict" title="Link to this definition">#</a></dt>
<dd><p>Call predict on the estimator with the best found parameters.</p>
<p>Only available if <code class="docutils literal notranslate"><span class="pre">refit=True</span></code> and the underlying estimator supports
<code class="docutils literal notranslate"><span class="pre">predict</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">indexable, length n_samples</span></dt><dd><p>Must fulfill the input assumptions of the
underlying estimator.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y_pred</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>The predicted labels or values for <code class="docutils literal notranslate"><span class="pre">X</span></code> based on the estimator with
the best found parameters.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV.predict_log_proba">
<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/model_selection/_search.py#L620"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.predict_log_proba" title="Link to this definition">#</a></dt>
<dd><p>Call predict_log_proba on the estimator with the best found parameters.</p>
<p>Only available if <code class="docutils literal notranslate"><span class="pre">refit=True</span></code> and the underlying estimator supports
<code class="docutils literal notranslate"><span class="pre">predict_log_proba</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">indexable, length n_samples</span></dt><dd><p>Must fulfill the input assumptions of the
underlying estimator.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y_pred</strong><span class="classifier">ndarray of shape (n_samples,) or (n_samples, n_classes)</span></dt><dd><p>Predicted class log-probabilities for <code class="docutils literal notranslate"><span class="pre">X</span></code> based on the estimator
with the best found parameters. The order of the classes
corresponds to that in the fitted attribute <a class="reference internal" href="../../glossary.html#term-classes_"><span class="xref std std-term">classes_</span></a>.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV.predict_proba">
<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/model_selection/_search.py#L597"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.predict_proba" title="Link to this definition">#</a></dt>
<dd><p>Call predict_proba on the estimator with the best found parameters.</p>
<p>Only available if <code class="docutils literal notranslate"><span class="pre">refit=True</span></code> and the underlying estimator supports
<code class="docutils literal notranslate"><span class="pre">predict_proba</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">indexable, length n_samples</span></dt><dd><p>Must fulfill the input assumptions of the
underlying estimator.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y_pred</strong><span class="classifier">ndarray of shape (n_samples,) or (n_samples, n_classes)</span></dt><dd><p>Predicted class probabilities for <code class="docutils literal notranslate"><span class="pre">X</span></code> based on the estimator with
the best found parameters. The order of the classes corresponds
to that in the fitted attribute <a class="reference internal" href="../../glossary.html#term-classes_"><span class="xref std std-term">classes_</span></a>.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV.score">
<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/model_selection/_search.py#L493"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.score" title="Link to this definition">#</a></dt>
<dd><p>Return the score on the given data, if the estimator has been refit.</p>
<p>This uses the score defined by <code class="docutils literal notranslate"><span class="pre">scoring</span></code> where provided, and the
<code class="docutils literal notranslate"><span class="pre">best_estimator_.score</span></code> method otherwise.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Input data, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
<code class="docutils literal notranslate"><span class="pre">n_features</span></code> is the number of features.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples, n_output) or (n_samples,), default=None</span></dt><dd><p>Target relative to X for classification or regression;
None for unsupervised learning.</p>
</dd>
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Parameters to be passed to the underlying scorer(s).</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.4: </span>Only available if <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code>. See
<a class="reference internal" href="../../metadata_routing.html#metadata-routing"><span class="std std-ref">Metadata Routing User Guide</span></a> for more
details.</p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>score</strong><span class="classifier">float</span></dt><dd><p>The score defined by <code class="docutils literal notranslate"><span class="pre">scoring</span></code> if provided, and the
<code class="docutils literal notranslate"><span class="pre">best_estimator_.score</span></code> method otherwise.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV.score_samples">
<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/model_selection/_search.py#L552"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.score_samples" title="Link to this definition">#</a></dt>
<dd><p>Call score_samples on the estimator with the best found parameters.</p>
<p>Only available if <code class="docutils literal notranslate"><span class="pre">refit=True</span></code> and the underlying estimator supports
<code class="docutils literal notranslate"><span class="pre">score_samples</span></code>.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.24.</span></p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">iterable</span></dt><dd><p>Data to predict on. Must fulfill input requirements
of the underlying estimator.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y_score</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>The <code class="docutils literal notranslate"><span class="pre">best_estimator_.score_samples</span></code> method.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV.set_params">
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/base.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.set_params" title="Link to this definition">#</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as <a class="reference internal" href="sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
parameters of the form <code class="docutils literal notranslate"><span class="pre"><component>__<parameter></span></code> so that it’s
possible to update each component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">estimator instance</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV.transform">
<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/model_selection/_search.py#L666"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.transform" title="Link to this definition">#</a></dt>
<dd><p>Call transform on the estimator with the best found parameters.</p>
<p>Only available if the underlying estimator supports <code class="docutils literal notranslate"><span class="pre">transform</span></code> and
<code class="docutils literal notranslate"><span class="pre">refit=True</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">indexable, length n_samples</span></dt><dd><p>Must fulfill the input assumptions of the
underlying estimator.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>Xt</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">X</span></code> transformed in the new space based on the estimator with
the best found parameters.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
</dd></dl>
<section id="gallery-examples">
<h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link to this heading">#</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 0.24! Many bug fixes and improvements were added, as well as some new key features. We detail below a few of the major features of this release. For an exhaustive list of all the changes, please refer to the release notes <release_notes_0_24>."><img alt="" src="../../_images/sphx_glr_plot_release_highlights_0_24_0_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/release_highlights/plot_release_highlights_0_24_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-0-24-0-py"><span class="std std-ref">Release Highlights for scikit-learn 0.24</span></a></p>
<div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 0.24</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example compares the parameter search performed by HalvingGridSearchCV and GridSearchCV."><img alt="" src="../../_images/sphx_glr_plot_successive_halving_heatmap_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/model_selection/plot_successive_halving_heatmap.html#sphx-glr-auto-examples-model-selection-plot-successive-halving-heatmap-py"><span class="std std-ref">Comparison between grid search and successive halving</span></a></p>
<div class="sphx-glr-thumbnail-title">Comparison between grid search and successive halving</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates how a successive halving search (~sklearn.model_selection.HalvingGridSearchCV and HalvingRandomSearchCV) iteratively chooses the best parameter combination out of multiple candidates."><img alt="" src="../../_images/sphx_glr_plot_successive_halving_iterations_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/model_selection/plot_successive_halving_iterations.html#sphx-glr-auto-examples-model-selection-plot-successive-halving-iterations-py"><span class="std std-ref">Successive Halving Iterations</span></a></p>
<div class="sphx-glr-thumbnail-title">Successive Halving Iterations</div>
</div></div></section>
</section>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.model_selection.HalvingGridSearchCV"><code class="docutils literal notranslate"><span class="pre">HalvingGridSearchCV</span></code></a><ul class="nav section-nav flex-column visible">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.model_selection.HalvingGridSearchCV.classes_"><code class="docutils literal notranslate"><span class="pre">classes_</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.model_selection.HalvingGridSearchCV.decision_function"><code class="docutils literal notranslate"><span class="pre">decision_function</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.model_selection.HalvingGridSearchCV.fit"><code class="docutils literal notranslate"><span class="pre">fit</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.model_selection.HalvingGridSearchCV.get_metadata_routing"><code class="docutils literal notranslate"><span class="pre">get_metadata_routing</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.model_selection.HalvingGridSearchCV.get_params"><code class="docutils literal notranslate"><span class="pre">get_params</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.model_selection.HalvingGridSearchCV.inverse_transform"><code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.model_selection.HalvingGridSearchCV.n_features_in_"><code class="docutils literal notranslate"><span class="pre">n_features_in_</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.model_selection.HalvingGridSearchCV.predict"><code class="docutils literal notranslate"><span class="pre">predict</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.model_selection.HalvingGridSearchCV.predict_log_proba"><code class="docutils literal notranslate"><span class="pre">predict_log_proba</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.model_selection.HalvingGridSearchCV.predict_proba"><code class="docutils literal notranslate"><span class="pre">predict_proba</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.model_selection.HalvingGridSearchCV.score"><code class="docutils literal notranslate"><span class="pre">score</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.model_selection.HalvingGridSearchCV.score_samples"><code class="docutils literal notranslate"><span class="pre">score_samples</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.model_selection.HalvingGridSearchCV.set_params"><code class="docutils literal notranslate"><span class="pre">set_params</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.model_selection.HalvingGridSearchCV.transform"><code class="docutils literal notranslate"><span class="pre">transform</span></code></a></li>
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