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<section id="iterativeimputer">
<h1>IterativeImputer<a class="headerlink" href="#iterativeimputer" title="Link to this heading">#</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.impute.IterativeImputer">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.impute.</span></span><span class="sig-name descname"><span class="pre">IterativeImputer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</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></em>, <em class="sig-param"><span class="n"><span class="pre">missing_values</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">sample_posterior</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">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_nearest_features</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">initial_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fill_value</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">imputation_order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ascending'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_complete</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">min_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">inf</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>, <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">add_indicator</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">keep_empty_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/impute/_iterative.py#L59"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.IterativeImputer" title="Link to this definition">#</a></dt>
<dd><p>Multivariate imputer that estimates each feature from all the others.</p>
<p>A strategy for imputing missing values by modeling each feature with
missing values as a function of other features in a round-robin fashion.</p>
<p>Read more in the <a class="reference internal" href="../impute.html#iterative-imputer"><span class="std std-ref">User Guide</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.21.</span></p>
</div>
<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_iterative_imputer</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_iterative_imputer</span> <span class="c1"># noqa</span>
<span class="gp">>>> </span><span class="c1"># now you can import normally from sklearn.impute</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.impute</span> <span class="kn">import</span> <span class="n">IterativeImputer</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, default=BayesianRidge()</span></dt><dd><p>The estimator to use at each step of the round-robin imputation.
If <code class="docutils literal notranslate"><span class="pre">sample_posterior=True</span></code>, the estimator must support
<code class="docutils literal notranslate"><span class="pre">return_std</span></code> in its <code class="docutils literal notranslate"><span class="pre">predict</span></code> method.</p>
</dd>
<dt><strong>missing_values</strong><span class="classifier">int or np.nan, default=np.nan</span></dt><dd><p>The placeholder for the missing values. All occurrences of
<code class="docutils literal notranslate"><span class="pre">missing_values</span></code> will be imputed. For pandas’ dataframes with
nullable integer dtypes with missing values, <code class="docutils literal notranslate"><span class="pre">missing_values</span></code>
should be set to <code class="docutils literal notranslate"><span class="pre">np.nan</span></code>, since <code class="docutils literal notranslate"><span class="pre">pd.NA</span></code> will be converted to <code class="docutils literal notranslate"><span class="pre">np.nan</span></code>.</p>
</dd>
<dt><strong>sample_posterior</strong><span class="classifier">bool, default=False</span></dt><dd><p>Whether to sample from the (Gaussian) predictive posterior of the
fitted estimator for each imputation. Estimator must support
<code class="docutils literal notranslate"><span class="pre">return_std</span></code> in its <code class="docutils literal notranslate"><span class="pre">predict</span></code> method if set to <code class="docutils literal notranslate"><span class="pre">True</span></code>. Set to
<code class="docutils literal notranslate"><span class="pre">True</span></code> if using <code class="docutils literal notranslate"><span class="pre">IterativeImputer</span></code> for multiple imputations.</p>
</dd>
<dt><strong>max_iter</strong><span class="classifier">int, default=10</span></dt><dd><p>Maximum number of imputation rounds to perform before returning the
imputations computed during the final round. A round is a single
imputation of each feature with missing values. The stopping criterion
is met once <code class="docutils literal notranslate"><span class="pre">max(abs(X_t</span> <span class="pre">-</span> <span class="pre">X_{t-1}))/max(abs(X[known_vals]))</span> <span class="pre"><</span> <span class="pre">tol</span></code>,
where <code class="docutils literal notranslate"><span class="pre">X_t</span></code> is <code class="docutils literal notranslate"><span class="pre">X</span></code> at iteration <code class="docutils literal notranslate"><span class="pre">t</span></code>. Note that early stopping is only
applied if <code class="docutils literal notranslate"><span class="pre">sample_posterior=False</span></code>.</p>
</dd>
<dt><strong>tol</strong><span class="classifier">float, default=1e-3</span></dt><dd><p>Tolerance of the stopping condition.</p>
</dd>
<dt><strong>n_nearest_features</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of other features to use to estimate the missing values of
each feature column. Nearness between features is measured using
the absolute correlation coefficient between each feature pair (after
initial imputation). To ensure coverage of features throughout the
imputation process, the neighbor features are not necessarily nearest,
but are drawn with probability proportional to correlation for each
imputed target feature. Can provide significant speed-up when the
number of features is huge. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, all features will be used.</p>
</dd>
<dt><strong>initial_strategy</strong><span class="classifier">{‘mean’, ‘median’, ‘most_frequent’, ‘constant’}, default=’mean’</span></dt><dd><p>Which strategy to use to initialize the missing values. Same as the
<code class="docutils literal notranslate"><span class="pre">strategy</span></code> parameter in <a class="reference internal" href="sklearn.impute.SimpleImputer.html#sklearn.impute.SimpleImputer" title="sklearn.impute.SimpleImputer"><code class="xref py py-class docutils literal notranslate"><span class="pre">SimpleImputer</span></code></a>.</p>
</dd>
<dt><strong>fill_value</strong><span class="classifier">str or numerical value, default=None</span></dt><dd><p>When <code class="docutils literal notranslate"><span class="pre">strategy="constant"</span></code>, <code class="docutils literal notranslate"><span class="pre">fill_value</span></code> is used to replace all
occurrences of missing_values. For string or object data types,
<code class="docutils literal notranslate"><span class="pre">fill_value</span></code> must be a string.
If <code class="docutils literal notranslate"><span class="pre">None</span></code>, <code class="docutils literal notranslate"><span class="pre">fill_value</span></code> will be 0 when imputing numerical
data and “missing_value” for strings or object data types.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.3.</span></p>
</div>
</dd>
<dt><strong>imputation_order</strong><span class="classifier">{‘ascending’, ‘descending’, ‘roman’, ‘arabic’, ‘random’}, default=’ascending’</span></dt><dd><p>The order in which the features will be imputed. Possible values:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">'ascending'</span></code>: From features with fewest missing values to most.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">'descending'</span></code>: From features with most missing values to fewest.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">'roman'</span></code>: Left to right.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">'arabic'</span></code>: Right to left.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">'random'</span></code>: A random order for each round.</p></li>
</ul>
</dd>
<dt><strong>skip_complete</strong><span class="classifier">bool, default=False</span></dt><dd><p>If <code class="docutils literal notranslate"><span class="pre">True</span></code> then features with missing values during <a class="reference internal" href="#sklearn.impute.IterativeImputer.transform" title="sklearn.impute.IterativeImputer.transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">transform</span></code></a>
which did not have any missing values during <a class="reference internal" href="#sklearn.impute.IterativeImputer.fit" title="sklearn.impute.IterativeImputer.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> will be
imputed with the initial imputation method only. Set to <code class="docutils literal notranslate"><span class="pre">True</span></code> if you
have many features with no missing values at both <a class="reference internal" href="#sklearn.impute.IterativeImputer.fit" title="sklearn.impute.IterativeImputer.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> and
<a class="reference internal" href="#sklearn.impute.IterativeImputer.transform" title="sklearn.impute.IterativeImputer.transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">transform</span></code></a> time to save compute.</p>
</dd>
<dt><strong>min_value</strong><span class="classifier">float or array-like of shape (n_features,), default=-np.inf</span></dt><dd><p>Minimum possible imputed value. Broadcast to shape <code class="docutils literal notranslate"><span class="pre">(n_features,)</span></code> if
scalar. If array-like, expects shape <code class="docutils literal notranslate"><span class="pre">(n_features,)</span></code>, one min value for
each feature. The default is <code class="docutils literal notranslate"><span class="pre">-np.inf</span></code>.</p>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 0.23: </span>Added support for array-like.</p>
</div>
</dd>
<dt><strong>max_value</strong><span class="classifier">float or array-like of shape (n_features,), default=np.inf</span></dt><dd><p>Maximum possible imputed value. Broadcast to shape <code class="docutils literal notranslate"><span class="pre">(n_features,)</span></code> if
scalar. If array-like, expects shape <code class="docutils literal notranslate"><span class="pre">(n_features,)</span></code>, one max value for
each feature. The default is <code class="docutils literal notranslate"><span class="pre">np.inf</span></code>.</p>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 0.23: </span>Added support for array-like.</p>
</div>
</dd>
<dt><strong>verbose</strong><span class="classifier">int, default=0</span></dt><dd><p>Verbosity flag, controls the debug messages that are issued
as functions are evaluated. The higher, the more verbose. Can be 0, 1,
or 2.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=None</span></dt><dd><p>The seed of the pseudo random number generator to use. Randomizes
selection of estimator features if <code class="docutils literal notranslate"><span class="pre">n_nearest_features</span></code> is not <code class="docutils literal notranslate"><span class="pre">None</span></code>,
the <code class="docutils literal notranslate"><span class="pre">imputation_order</span></code> if <code class="docutils literal notranslate"><span class="pre">random</span></code>, and the sampling from posterior if
<code class="docutils literal notranslate"><span class="pre">sample_posterior=True</span></code>. Use an integer for determinism.
See <a class="reference internal" href="../../glossary.html#term-random_state"><span class="xref std std-term">the Glossary</span></a>.</p>
</dd>
<dt><strong>add_indicator</strong><span class="classifier">bool, default=False</span></dt><dd><p>If <code class="docutils literal notranslate"><span class="pre">True</span></code>, a <a class="reference internal" href="sklearn.impute.MissingIndicator.html#sklearn.impute.MissingIndicator" title="sklearn.impute.MissingIndicator"><code class="xref py py-class docutils literal notranslate"><span class="pre">MissingIndicator</span></code></a> transform will stack onto output
of the imputer’s transform. This allows a predictive estimator
to account for missingness despite imputation. If a feature has no
missing values at fit/train time, the feature won’t appear on
the missing indicator even if there are missing values at
transform/test time.</p>
</dd>
<dt><strong>keep_empty_features</strong><span class="classifier">bool, default=False</span></dt><dd><p>If True, features that consist exclusively of missing values when
<code class="docutils literal notranslate"><span class="pre">fit</span></code> is called are returned in results when <code class="docutils literal notranslate"><span class="pre">transform</span></code> is called.
The imputed value is always <code class="docutils literal notranslate"><span class="pre">0</span></code> except when
<code class="docutils literal notranslate"><span class="pre">initial_strategy="constant"</span></code> in which case <code class="docutils literal notranslate"><span class="pre">fill_value</span></code> will be
used instead.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.2.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>initial_imputer_</strong><span class="classifier">object of type <a class="reference internal" href="sklearn.impute.SimpleImputer.html#sklearn.impute.SimpleImputer" title="sklearn.impute.SimpleImputer"><code class="xref py py-class docutils literal notranslate"><span class="pre">SimpleImputer</span></code></a></span></dt><dd><p>Imputer used to initialize the missing values.</p>
</dd>
<dt><strong>imputation_sequence_</strong><span class="classifier">list of tuples</span></dt><dd><p>Each tuple has <code class="docutils literal notranslate"><span class="pre">(feat_idx,</span> <span class="pre">neighbor_feat_idx,</span> <span class="pre">estimator)</span></code>, where
<code class="docutils literal notranslate"><span class="pre">feat_idx</span></code> is the current feature to be imputed,
<code class="docutils literal notranslate"><span class="pre">neighbor_feat_idx</span></code> is the array of other features used to impute the
current feature, and <code class="docutils literal notranslate"><span class="pre">estimator</span></code> is the trained estimator used for
the imputation. Length is <code class="docutils literal notranslate"><span class="pre">self.n_features_with_missing_</span> <span class="pre">*</span>
<span class="pre">self.n_iter_</span></code>.</p>
</dd>
<dt><strong>n_iter_</strong><span class="classifier">int</span></dt><dd><p>Number of iteration rounds that occurred. Will be less than
<code class="docutils literal notranslate"><span class="pre">self.max_iter</span></code> if early stopping criterion was reached.</p>
</dd>
<dt><strong>n_features_in_</strong><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>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.24.</span></p>
</div>
</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>. Defined only when <code class="docutils literal notranslate"><span class="pre">X</span></code>
has feature names that are all strings.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.0.</span></p>
</div>
</dd>
<dt><strong>n_features_with_missing_</strong><span class="classifier">int</span></dt><dd><p>Number of features with missing values.</p>
</dd>
<dt><strong>indicator_</strong><span class="classifier"><a class="reference internal" href="sklearn.impute.MissingIndicator.html#sklearn.impute.MissingIndicator" title="sklearn.impute.MissingIndicator"><code class="xref py py-class docutils literal notranslate"><span class="pre">MissingIndicator</span></code></a></span></dt><dd><p>Indicator used to add binary indicators for missing values.
<code class="docutils literal notranslate"><span class="pre">None</span></code> if <code class="docutils literal notranslate"><span class="pre">add_indicator=False</span></code>.</p>
</dd>
<dt><strong>random_state_</strong><span class="classifier">RandomState instance</span></dt><dd><p>RandomState instance that is generated either from a seed, the random
number generator or by <code class="docutils literal notranslate"><span class="pre">np.random</span></code>.</p>
</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.impute.SimpleImputer.html#sklearn.impute.SimpleImputer" title="sklearn.impute.SimpleImputer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SimpleImputer</span></code></a></dt><dd><p>Univariate imputer for completing missing values with simple strategies.</p>
</dd>
<dt><a class="reference internal" href="sklearn.impute.KNNImputer.html#sklearn.impute.KNNImputer" title="sklearn.impute.KNNImputer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KNNImputer</span></code></a></dt><dd><p>Multivariate imputer that estimates missing features using nearest samples.</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>To support imputation in inductive mode we store each feature’s estimator
during the <a class="reference internal" href="#sklearn.impute.IterativeImputer.fit" title="sklearn.impute.IterativeImputer.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> phase, and predict without refitting (in order)
during the <a class="reference internal" href="#sklearn.impute.IterativeImputer.transform" title="sklearn.impute.IterativeImputer.transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">transform</span></code></a> phase.</p>
<p>Features which contain all missing values at <a class="reference internal" href="#sklearn.impute.IterativeImputer.fit" title="sklearn.impute.IterativeImputer.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> are discarded upon
<a class="reference internal" href="#sklearn.impute.IterativeImputer.transform" title="sklearn.impute.IterativeImputer.transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">transform</span></code></a>.</p>
<p>Using defaults, the imputer scales in <span class="math notranslate nohighlight">\(\mathcal{O}(knp^3\min(n,p))\)</span>
where <span class="math notranslate nohighlight">\(k\)</span> = <code class="docutils literal notranslate"><span class="pre">max_iter</span></code>, <span class="math notranslate nohighlight">\(n\)</span> the number of samples and
<span class="math notranslate nohighlight">\(p\)</span> the number of features. It thus becomes prohibitively costly when
the number of features increases. Setting
<code class="docutils literal notranslate"><span class="pre">n_nearest_features</span> <span class="pre"><<</span> <span class="pre">n_features</span></code>, <code class="docutils literal notranslate"><span class="pre">skip_complete=True</span></code> or increasing <code class="docutils literal notranslate"><span class="pre">tol</span></code>
can help to reduce its computational cost.</p>
<p>Depending on the nature of missing values, simple imputers can be
preferable in a prediction context.</p>
<p class="rubric">References</p>
<div role="list" class="citation-list">
<div class="citation" id="rcd31b817a31e-1" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p><a class="reference external" href="https://www.jstatsoft.org/article/view/v045i03">Stef van Buuren, Karin Groothuis-Oudshoorn (2011). “mice:
Multivariate Imputation by Chained Equations in R”. Journal of
Statistical Software 45: 1-67.</a></p>
</div>
<div class="citation" id="rcd31b817a31e-2" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
<p><a class="reference external" href="https://www.jstor.org/stable/2984099">S. F. Buck, (1960). “A Method of Estimation of Missing Values in
Multivariate Data Suitable for use with an Electronic Computer”.
Journal of the Royal Statistical Society 22(2): 302-306.</a></p>
</div>
</div>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</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_iterative_imputer</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.impute</span> <span class="kn">import</span> <span class="n">IterativeImputer</span>
<span class="gp">>>> </span><span class="n">imp_mean</span> <span class="o">=</span> <span class="n">IterativeImputer</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="n">imp_mean</span><span class="o">.</span><span class="n">fit</span><span class="p">([[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">9</span><span class="p">]])</span>
<span class="go">IterativeImputer(random_state=0)</span>
<span class="gp">>>> </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">9</span><span class="p">]]</span>
<span class="gp">>>> </span><span class="n">imp_mean</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="go">array([[ 6.9584..., 2. , 3. ],</span>
<span class="go"> [ 4. , 2.6000..., 6. ],</span>
<span class="go"> [10. , 4.9999..., 9. ]])</span>
</pre></div>
</div>
<p>For a more detailed example see
<a class="reference internal" href="../../auto_examples/impute/plot_missing_values.html#sphx-glr-auto-examples-impute-plot-missing-values-py"><span class="std std-ref">Imputing missing values before building an estimator</span></a> or
<a class="reference internal" href="../../auto_examples/impute/plot_iterative_imputer_variants_comparison.html#sphx-glr-auto-examples-impute-plot-iterative-imputer-variants-comparison-py"><span class="std std-ref">Imputing missing values with variants of IterativeImputer</span></a>.</p>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.impute.IterativeImputer.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">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/impute/_iterative.py#L905"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.IterativeImputer.fit" title="Link to this definition">#</a></dt>
<dd><p>Fit the imputer on <code class="docutils literal notranslate"><span class="pre">X</span></code> and return self.</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, 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">Ignored</span></dt><dd><p>Not used, present for API consistency by convention.</p>
</dd>
<dt><strong>**fit_params</strong><span class="classifier">dict</span></dt><dd><p>Parameters routed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method of the sub-estimator via the
metadata routing API.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.5: </span>Only available if
<code class="docutils literal notranslate"><span class="pre">sklearn.set_config(enable_metadata_routing=True)</span></code> is set. 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>self</strong><span class="classifier">object</span></dt><dd><p>Fitted estimator.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.impute.IterativeImputer.fit_transform">
<span class="sig-name descname"><span class="pre">fit_transform</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/impute/_iterative.py#L707"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.IterativeImputer.fit_transform" title="Link to this definition">#</a></dt>
<dd><p>Fit the imputer on <code class="docutils literal notranslate"><span class="pre">X</span></code> and return the transformed <code class="docutils literal notranslate"><span class="pre">X</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">array-like, 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">Ignored</span></dt><dd><p>Not used, present for API consistency by convention.</p>
</dd>
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Parameters routed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method of the sub-estimator via the
metadata routing API.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.5: </span>Only available if
<code class="docutils literal notranslate"><span class="pre">sklearn.set_config(enable_metadata_routing=True)</span></code> is set. 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>Xt</strong><span class="classifier">array-like, shape (n_samples, n_features)</span></dt><dd><p>The imputed input data.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.impute.IterativeImputer.get_feature_names_out">
<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</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/impute/_iterative.py#L935"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.IterativeImputer.get_feature_names_out" title="Link to this definition">#</a></dt>
<dd><p>Get output feature names for transformation.</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>input_features</strong><span class="classifier">array-like of str or None, default=None</span></dt><dd><p>Input features.</p>
<ul class="simple">
<li><p>If <code class="docutils literal notranslate"><span class="pre">input_features</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>, then <code class="docutils literal notranslate"><span class="pre">feature_names_in_</span></code> is
used as feature names in. If <code class="docutils literal notranslate"><span class="pre">feature_names_in_</span></code> is not defined,
then the following input feature names are generated:
<code class="docutils literal notranslate"><span class="pre">["x0",</span> <span class="pre">"x1",</span> <span class="pre">...,</span> <span class="pre">"x(n_features_in_</span> <span class="pre">-</span> <span class="pre">1)"]</span></code>.</p></li>
<li><p>If <code class="docutils literal notranslate"><span class="pre">input_features</span></code> is an array-like, then <code class="docutils literal notranslate"><span class="pre">input_features</span></code> must
match <code class="docutils literal notranslate"><span class="pre">feature_names_in_</span></code> if <code class="docutils literal notranslate"><span class="pre">feature_names_in_</span></code> is defined.</p></li>
</ul>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>feature_names_out</strong><span class="classifier">ndarray of str objects</span></dt><dd><p>Transformed feature names.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.impute.IterativeImputer.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/impute/_iterative.py#L960"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.IterativeImputer.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 1.5.</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.impute.IterativeImputer.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.impute.IterativeImputer.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.impute.IterativeImputer.set_output">
<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</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/utils/_set_output.py#L392"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.IterativeImputer.set_output" title="Link to this definition">#</a></dt>
<dd><p>Set output container.</p>
<p>See <a class="reference internal" href="../../auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
for an example on how to use the API.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>transform</strong><span class="classifier">{“default”, “pandas”, “polars”}, default=None</span></dt><dd><p>Configure output of <code class="docutils literal notranslate"><span class="pre">transform</span></code> and <code class="docutils literal notranslate"><span class="pre">fit_transform</span></code>.</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">"default"</span></code>: Default output format of a transformer</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">"pandas"</span></code>: DataFrame output</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">"polars"</span></code>: Polars output</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: Transform configuration is unchanged</p></li>
</ul>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.4: </span><code class="docutils literal notranslate"><span class="pre">"polars"</span></code> option was added.</p>
</div>
</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.impute.IterativeImputer.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.impute.IterativeImputer.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.impute.IterativeImputer.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/impute/_iterative.py#L851"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.IterativeImputer.transform" title="Link to this definition">#</a></dt>
<dd><p>Impute all missing values in <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
<p>Note that this is stochastic, and that if <code class="docutils literal notranslate"><span class="pre">random_state</span></code> is not fixed,
repeated calls, or permuted input, results will differ.</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 of shape (n_samples, n_features)</span></dt><dd><p>The input data to complete.</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">array-like, shape (n_samples, n_features)</span></dt><dd><p>The imputed input data.</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="Missing values can be replaced by the mean, the median or the most frequent value using the basic SimpleImputer."><img alt="" src="../../_images/sphx_glr_plot_missing_values_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/impute/plot_missing_values.html#sphx-glr-auto-examples-impute-plot-missing-values-py"><span class="std std-ref">Imputing missing values before building an estimator</span></a></p>
<div class="sphx-glr-thumbnail-title">Imputing missing values before building an estimator</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The IterativeImputer class is very flexible - it can be used with a variety of estimators to do round-robin regression, treating every variable as an output in turn."><img alt="" src="../../_images/sphx_glr_plot_iterative_imputer_variants_comparison_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/impute/plot_iterative_imputer_variants_comparison.html#sphx-glr-auto-examples-impute-plot-iterative-imputer-variants-comparison-py"><span class="std std-ref">Imputing missing values with variants of IterativeImputer</span></a></p>
<div class="sphx-glr-thumbnail-title">Imputing missing values with variants of IterativeImputer</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.impute.IterativeImputer"><code class="docutils literal notranslate"><span class="pre">IterativeImputer</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.impute.IterativeImputer.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.impute.IterativeImputer.fit_transform"><code class="docutils literal notranslate"><span class="pre">fit_transform</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.impute.IterativeImputer.get_feature_names_out"><code class="docutils literal notranslate"><span class="pre">get_feature_names_out</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.impute.IterativeImputer.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.impute.IterativeImputer.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.impute.IterativeImputer.set_output"><code class="docutils literal notranslate"><span class="pre">set_output</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.impute.IterativeImputer.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.impute.IterativeImputer.transform"><code class="docutils literal notranslate"><span class="pre">transform</span></code></a></li>
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