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<ul>
<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.preprocessing</span></code>.LabelBinarizer</a><ul>
<li><a class="reference internal" href="#sklearn.preprocessing.LabelBinarizer"><code class="docutils literal notranslate"><span class="pre">LabelBinarizer</span></code></a><ul>
<li><a class="reference internal" href="#sklearn.preprocessing.LabelBinarizer.fit"><code class="docutils literal notranslate"><span class="pre">LabelBinarizer.fit</span></code></a></li>
<li><a class="reference internal" href="#sklearn.preprocessing.LabelBinarizer.fit_transform"><code class="docutils literal notranslate"><span class="pre">LabelBinarizer.fit_transform</span></code></a></li>
<li><a class="reference internal" href="#sklearn.preprocessing.LabelBinarizer.get_metadata_routing"><code class="docutils literal notranslate"><span class="pre">LabelBinarizer.get_metadata_routing</span></code></a></li>
<li><a class="reference internal" href="#sklearn.preprocessing.LabelBinarizer.get_params"><code class="docutils literal notranslate"><span class="pre">LabelBinarizer.get_params</span></code></a></li>
<li><a class="reference internal" href="#sklearn.preprocessing.LabelBinarizer.inverse_transform"><code class="docutils literal notranslate"><span class="pre">LabelBinarizer.inverse_transform</span></code></a></li>
<li><a class="reference internal" href="#sklearn.preprocessing.LabelBinarizer.set_inverse_transform_request"><code class="docutils literal notranslate"><span class="pre">LabelBinarizer.set_inverse_transform_request</span></code></a></li>
<li><a class="reference internal" href="#sklearn.preprocessing.LabelBinarizer.set_output"><code class="docutils literal notranslate"><span class="pre">LabelBinarizer.set_output</span></code></a></li>
<li><a class="reference internal" href="#sklearn.preprocessing.LabelBinarizer.set_params"><code class="docutils literal notranslate"><span class="pre">LabelBinarizer.set_params</span></code></a></li>
<li><a class="reference internal" href="#sklearn.preprocessing.LabelBinarizer.transform"><code class="docutils literal notranslate"><span class="pre">LabelBinarizer.transform</span></code></a></li>
</ul>
</li>
<li><a class="reference internal" href="#examples-using-sklearn-preprocessing-labelbinarizer">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.preprocessing.LabelBinarizer</span></code></a></li>
</ul>
</li>
</ul>
</div>
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<section id="sklearn-preprocessing-labelbinarizer">
<h1><a class="reference internal" href="../classes.html#module-sklearn.preprocessing" title="sklearn.preprocessing"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.preprocessing</span></code></a>.LabelBinarizer<a class="headerlink" href="#sklearn-preprocessing-labelbinarizer" title="Link to this heading">¶</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.preprocessing.LabelBinarizer">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">LabelBinarizer</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">neg_label</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">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_output</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/1cb6e2a65/sklearn/preprocessing/_label.py#L168"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer" title="Link to this definition">¶</a></dt>
<dd><p>Binarize labels in a one-vs-all fashion.</p>
<p>Several regression and binary classification algorithms are
available in scikit-learn. A simple way to extend these algorithms
to the multi-class classification case is to use the so-called
one-vs-all scheme.</p>
<p>At learning time, this simply consists in learning one regressor
or binary classifier per class. In doing so, one needs to convert
multi-class labels to binary labels (belong or does not belong
to the class). <code class="docutils literal notranslate"><span class="pre">LabelBinarizer</span></code> makes this process easy with the
transform method.</p>
<p>At prediction time, one assigns the class for which the corresponding
model gave the greatest confidence. <code class="docutils literal notranslate"><span class="pre">LabelBinarizer</span></code> makes this easy
with the <a class="reference internal" href="#sklearn.preprocessing.LabelBinarizer.inverse_transform" title="sklearn.preprocessing.LabelBinarizer.inverse_transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">inverse_transform</span></code></a> method.</p>
<p>Read more in the <a class="reference internal" href="../preprocessing_targets.html#preprocessing-targets"><span class="std std-ref">User Guide</span></a>.</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>neg_label</strong><span class="classifier">int, default=0</span></dt><dd><p>Value with which negative labels must be encoded.</p>
</dd>
<dt><strong>pos_label</strong><span class="classifier">int, default=1</span></dt><dd><p>Value with which positive labels must be encoded.</p>
</dd>
<dt><strong>sparse_output</strong><span class="classifier">bool, default=False</span></dt><dd><p>True if the returned array from transform is desired to be in sparse
CSR format.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>classes_</strong><span class="classifier">ndarray of shape (n_classes,)</span></dt><dd><p>Holds the label for each class.</p>
</dd>
<dt><strong>y_type_</strong><span class="classifier">str</span></dt><dd><p>Represents the type of the target data as evaluated by
<a class="reference internal" href="sklearn.utils.multiclass.type_of_target.html#sklearn.utils.multiclass.type_of_target" title="sklearn.utils.multiclass.type_of_target"><code class="xref py py-func docutils literal notranslate"><span class="pre">type_of_target</span></code></a>. Possible type are
‘continuous’, ‘continuous-multioutput’, ‘binary’, ‘multiclass’,
‘multiclass-multioutput’, ‘multilabel-indicator’, and ‘unknown’.</p>
</dd>
<dt><strong>sparse_input_</strong><span class="classifier">bool</span></dt><dd><dl class="simple">
<dt><code class="docutils literal notranslate"><span class="pre">True</span></code> if the input data to transform is given as a sparse matrix,</dt><dd><p><code class="docutils literal notranslate"><span class="pre">False</span></code> otherwise.</p>
</dd>
</dl>
</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.preprocessing.label_binarize.html#sklearn.preprocessing.label_binarize" title="sklearn.preprocessing.label_binarize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">label_binarize</span></code></a></dt><dd><p>Function to perform the transform operation of LabelBinarizer with fixed classes.</p>
</dd>
<dt><a class="reference internal" href="sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder" title="sklearn.preprocessing.OneHotEncoder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">OneHotEncoder</span></code></a></dt><dd><p>Encode categorical features using a one-hot aka one-of-K scheme.</p>
</dd>
</dl>
</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">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">LabelBinarizer</span>
<span class="gp">>>> </span><span class="n">lb</span> <span class="o">=</span> <span class="n">LabelBinarizer</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">lb</span><span class="o">.</span><span class="n">fit</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
<span class="go">LabelBinarizer()</span>
<span class="gp">>>> </span><span class="n">lb</span><span class="o">.</span><span class="n">classes_</span>
<span class="go">array([1, 2, 4, 6])</span>
<span class="gp">>>> </span><span class="n">lb</span><span class="o">.</span><span class="n">transform</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">])</span>
<span class="go">array([[1, 0, 0, 0],</span>
<span class="go"> [0, 0, 0, 1]])</span>
</pre></div>
</div>
<p>Binary targets transform to a column vector</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">lb</span> <span class="o">=</span> <span class="n">LabelBinarizer</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">lb</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">([</span><span class="s1">'yes'</span><span class="p">,</span> <span class="s1">'no'</span><span class="p">,</span> <span class="s1">'no'</span><span class="p">,</span> <span class="s1">'yes'</span><span class="p">])</span>
<span class="go">array([[1],</span>
<span class="go"> [0],</span>
<span class="go"> [0],</span>
<span class="go"> [1]])</span>
</pre></div>
</div>
<p>Passing a 2D matrix for multilabel classification</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="n">lb</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]))</span>
<span class="go">LabelBinarizer()</span>
<span class="gp">>>> </span><span class="n">lb</span><span class="o">.</span><span class="n">classes_</span>
<span class="go">array([0, 1, 2])</span>
<span class="gp">>>> </span><span class="n">lb</span><span class="o">.</span><span class="n">transform</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
<span class="go">array([[1, 0, 0],</span>
<span class="go"> [0, 1, 0],</span>
<span class="go"> [0, 0, 1],</span>
<span class="go"> [0, 1, 0]])</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.preprocessing.LabelBinarizer.fit" title="sklearn.preprocessing.LabelBinarizer.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(y)</p></td>
<td><p>Fit label binarizer.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.preprocessing.LabelBinarizer.fit_transform" title="sklearn.preprocessing.LabelBinarizer.fit_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_transform</span></code></a>(y)</p></td>
<td><p>Fit label binarizer/transform multi-class labels to binary labels.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.preprocessing.LabelBinarizer.get_metadata_routing" title="sklearn.preprocessing.LabelBinarizer.get_metadata_routing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_metadata_routing</span></code></a>()</p></td>
<td><p>Get metadata routing of this object.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.preprocessing.LabelBinarizer.get_params" title="sklearn.preprocessing.LabelBinarizer.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>([deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.preprocessing.LabelBinarizer.inverse_transform" title="sklearn.preprocessing.LabelBinarizer.inverse_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">inverse_transform</span></code></a>(Y[, threshold])</p></td>
<td><p>Transform binary labels back to multi-class labels.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.preprocessing.LabelBinarizer.set_inverse_transform_request" title="sklearn.preprocessing.LabelBinarizer.set_inverse_transform_request"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_inverse_transform_request</span></code></a>(*[, threshold])</p></td>
<td><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> method.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.preprocessing.LabelBinarizer.set_output" title="sklearn.preprocessing.LabelBinarizer.set_output"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_output</span></code></a>(*[, transform])</p></td>
<td><p>Set output container.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.preprocessing.LabelBinarizer.set_params" title="sklearn.preprocessing.LabelBinarizer.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(**params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.preprocessing.LabelBinarizer.transform" title="sklearn.preprocessing.LabelBinarizer.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(y)</p></td>
<td><p>Transform multi-class labels to binary labels.</p></td>
</tr>
</tbody>
</table>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.preprocessing.LabelBinarizer.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">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/1cb6e2a65/sklearn/preprocessing/_label.py#L268"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer.fit" title="Link to this definition">¶</a></dt>
<dd><p>Fit label binarizer.</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>y</strong><span class="classifier">ndarray of shape (n_samples,) or (n_samples, n_classes)</span></dt><dd><p>Target values. The 2-d matrix should only contain 0 and 1,
represents multilabel classification.</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>Returns the instance itself.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.preprocessing.LabelBinarizer.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">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/1cb6e2a65/sklearn/preprocessing/_label.py#L309"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer.fit_transform" title="Link to this definition">¶</a></dt>
<dd><p>Fit label binarizer/transform multi-class labels to binary labels.</p>
<p>The output of transform is sometimes referred to as
the 1-of-K coding scheme.</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>y</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)</span></dt><dd><p>Target values. The 2-d matrix should only contain 0 and 1,
represents multilabel classification. Sparse matrix can be
CSR, CSC, COO, DOK, or LIL.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>Y</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples, n_classes)</span></dt><dd><p>Shape will be (n_samples, 1) for binary problems. Sparse matrix
will be of CSR format.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.preprocessing.LabelBinarizer.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/1cb6e2a65/sklearn/utils/_metadata_requests.py#L1466"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer.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>
<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">MetadataRequest</span></dt><dd><p>A <a class="reference internal" href="sklearn.utils.metadata_routing.MetadataRequest.html#sklearn.utils.metadata_routing.MetadataRequest" title="sklearn.utils.metadata_routing.MetadataRequest"><code class="xref py py-class docutils literal notranslate"><span class="pre">MetadataRequest</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.preprocessing.LabelBinarizer.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/1cb6e2a65/sklearn/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer.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.preprocessing.LabelBinarizer.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">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</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/1cb6e2a65/sklearn/preprocessing/_label.py#L365"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer.inverse_transform" title="Link to this definition">¶</a></dt>
<dd><p>Transform binary labels back to multi-class labels.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>Y</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples, n_classes)</span></dt><dd><p>Target values. All sparse matrices are converted to CSR before
inverse transformation.</p>
</dd>
<dt><strong>threshold</strong><span class="classifier">float, default=None</span></dt><dd><p>Threshold used in the binary and multi-label cases.</p>
<p>Use 0 when <code class="docutils literal notranslate"><span class="pre">Y</span></code> contains the output of <a class="reference internal" href="../../glossary.html#term-decision_function"><span class="xref std std-term">decision_function</span></a>
(classifier).
Use 0.5 when <code class="docutils literal notranslate"><span class="pre">Y</span></code> contains the output of <a class="reference internal" href="../../glossary.html#term-predict_proba"><span class="xref std std-term">predict_proba</span></a>.</p>
<p>If None, the threshold is assumed to be half way between
neg_label and pos_label.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples,)</span></dt><dd><p>Target values. Sparse matrix will be of CSR format.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>In the case when the binary labels are fractional
(probabilistic), <a class="reference internal" href="#sklearn.preprocessing.LabelBinarizer.inverse_transform" title="sklearn.preprocessing.LabelBinarizer.inverse_transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">inverse_transform</span></code></a> chooses the class with the
greatest value. Typically, this allows to use the output of a
linear model’s <a class="reference internal" href="../../glossary.html#term-decision_function"><span class="xref std std-term">decision_function</span></a> method directly as the input
of <a class="reference internal" href="#sklearn.preprocessing.LabelBinarizer.inverse_transform" title="sklearn.preprocessing.LabelBinarizer.inverse_transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">inverse_transform</span></code></a>.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.preprocessing.LabelBinarizer.set_inverse_transform_request">
<span class="sig-name descname"><span class="pre">set_inverse_transform_request</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">threshold</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.preprocessing.LabelBinarizer" title="sklearn.preprocessing._label.LabelBinarizer"><span class="pre">LabelBinarizer</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/1cb6e2a65/sklearn/utils/_metadata_requests.py#L1235"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer.set_inverse_transform_request" title="Link to this definition">¶</a></dt>
<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> method.</p>
<p>Note that this method is only relevant if
<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
Please see <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>
<p>The options for each parameter are:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
</ul>
<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
existing request. This allows you to change the request for some
parameters and not others.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.3.</span></p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
<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>. Otherwise it has no effect.</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>threshold</strong><span class="classifier">str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">threshold</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code>.</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>The updated object.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.preprocessing.LabelBinarizer.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/1cb6e2a65/sklearn/utils/_set_output.py#L346"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer.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”}, 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">New 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.preprocessing.LabelBinarizer.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/1cb6e2a65/sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer.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.preprocessing.LabelBinarizer.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">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/1cb6e2a65/sklearn/preprocessing/_label.py#L331"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer.transform" title="Link to this definition">¶</a></dt>
<dd><p>Transform multi-class labels to binary labels.</p>
<p>The output of transform is sometimes referred to by some authors as
the 1-of-K coding scheme.</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>y</strong><span class="classifier">{array, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)</span></dt><dd><p>Target values. The 2-d matrix should only contain 0 and 1,
represents multilabel classification. Sparse matrix can be
CSR, CSC, COO, DOK, or LIL.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>Y</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples, n_classes)</span></dt><dd><p>Shape will be (n_samples, 1) for binary problems. Sparse matrix
will be of CSR format.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
</dd></dl>
<section id="examples-using-sklearn-preprocessing-labelbinarizer">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.preprocessing.LabelBinarizer</span></code><a class="headerlink" href="#examples-using-sklearn-preprocessing-labelbinarizer" title="Link to this heading">¶</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example describes the use of the Receiver Operating Characteristic (ROC) metric to evaluat..."><img alt="" src="../../_images/sphx_glr_plot_roc_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/model_selection/plot_roc.html#sphx-glr-auto-examples-model-selection-plot-roc-py"><span class="std std-ref">Multiclass Receiver Operating Characteristic (ROC)</span></a></p>
<div class="sphx-glr-thumbnail-title">Multiclass Receiver Operating Characteristic (ROC)</div>
</div></div><div class="clearer"></div></section>
</section>
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