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<li><a class="reference internal" href="#">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a><ul>
<li><a class="reference internal" href="#label-binarization">6.9.1. Label binarization</a><ul>
<li><a class="reference internal" href="#labelbinarizer">6.9.1.1. LabelBinarizer</a></li>
<li><a class="reference internal" href="#multilabelbinarizer">6.9.1.2. MultiLabelBinarizer</a></li>
</ul>
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<li><a class="reference internal" href="#label-encoding">6.9.2. Label encoding</a></li>
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<section id="transforming-the-prediction-target-y">
<span id="preprocessing-targets"></span><h1><span class="section-number">6.9. </span>Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)<a class="headerlink" href="#transforming-the-prediction-target-y" title="Link to this heading">¶</a></h1>
<p>These are transformers that are not intended to be used on features, only on
supervised learning targets. See also <a class="reference internal" href="compose.html#transformed-target-regressor"><span class="std std-ref">Transforming target in regression</span></a> if
you want to transform the prediction target for learning, but evaluate the
model in the original (untransformed) space.</p>
<section id="label-binarization">
<h2><span class="section-number">6.9.1. </span>Label binarization<a class="headerlink" href="#label-binarization" title="Link to this heading">¶</a></h2>
<section id="labelbinarizer">
<h3><span class="section-number">6.9.1.1. </span>LabelBinarizer<a class="headerlink" href="#labelbinarizer" title="Link to this heading">¶</a></h3>
<p><a class="reference internal" href="generated/sklearn.preprocessing.LabelBinarizer.html#sklearn.preprocessing.LabelBinarizer" title="sklearn.preprocessing.LabelBinarizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelBinarizer</span></code></a> is a utility class to help create a <a class="reference internal" href="../glossary.html#term-label-indicator-matrix"><span class="xref std std-term">label
indicator matrix</span></a> from a list of <a class="reference internal" href="../glossary.html#term-multiclass"><span class="xref std std-term">multiclass</span></a> labels:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">preprocessing</span>
<span class="gp">>>> </span><span class="n">lb</span> <span class="o">=</span> <span class="n">preprocessing</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>Using this format can enable multiclass classification in estimators
that support the label indicator matrix format.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>LabelBinarizer is not needed if you are using an estimator that
already supports <a class="reference internal" href="../glossary.html#term-multiclass"><span class="xref std std-term">multiclass</span></a> data.</p>
</div>
<p>For more information about multiclass classification, refer to
<a class="reference internal" href="multiclass.html#multiclass-classification"><span class="std std-ref">Multiclass classification</span></a>.</p>
</section>
<section id="multilabelbinarizer">
<h3><span class="section-number">6.9.1.2. </span>MultiLabelBinarizer<a class="headerlink" href="#multilabelbinarizer" title="Link to this heading">¶</a></h3>
<p>In <a class="reference internal" href="../glossary.html#term-multilabel"><span class="xref std std-term">multilabel</span></a> learning, the joint set of binary classification tasks is
expressed with a label binary indicator array: each sample is one row of a 2d
array of shape (n_samples, n_classes) with binary values where the one, i.e. the
non zero elements, corresponds to the subset of labels for that sample. An array
such as <code class="docutils literal notranslate"><span class="pre">np.array([[1,</span> <span class="pre">0,</span> <span class="pre">0],</span> <span class="pre">[0,</span> <span class="pre">1,</span> <span class="pre">1],</span> <span class="pre">[0,</span> <span class="pre">0,</span> <span class="pre">0]])</span></code> represents label 0 in the
first sample, labels 1 and 2 in the second sample, and no labels in the third
sample.</p>
<p>Producing multilabel data as a list of sets of labels may be more intuitive.
The <a class="reference internal" href="generated/sklearn.preprocessing.MultiLabelBinarizer.html#sklearn.preprocessing.MultiLabelBinarizer" title="sklearn.preprocessing.MultiLabelBinarizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">MultiLabelBinarizer</span></code></a>
transformer can be used to convert between a collection of collections of
labels and the indicator format:</p>
<div class="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">MultiLabelBinarizer</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</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="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">],</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">3</span><span class="p">],</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">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</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="gp">>>> </span><span class="n">MultiLabelBinarizer</span><span class="p">()</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="go">array([[0, 0, 1, 1, 1],</span>
<span class="go"> [0, 0, 1, 0, 0],</span>
<span class="go"> [1, 1, 0, 1, 0],</span>
<span class="go"> [1, 1, 1, 1, 1],</span>
<span class="go"> [1, 1, 1, 0, 0]])</span>
</pre></div>
</div>
<p>For more information about multilabel classification, refer to
<a class="reference internal" href="multiclass.html#multilabel-classification"><span class="std std-ref">Multilabel classification</span></a>.</p>
</section>
</section>
<section id="label-encoding">
<h2><span class="section-number">6.9.2. </span>Label encoding<a class="headerlink" href="#label-encoding" title="Link to this heading">¶</a></h2>
<p><a class="reference internal" href="generated/sklearn.preprocessing.LabelEncoder.html#sklearn.preprocessing.LabelEncoder" title="sklearn.preprocessing.LabelEncoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelEncoder</span></code></a> is a utility class to help normalize labels such that
they contain only values between 0 and n_classes-1. This is sometimes useful
for writing efficient Cython routines. <a class="reference internal" href="generated/sklearn.preprocessing.LabelEncoder.html#sklearn.preprocessing.LabelEncoder" title="sklearn.preprocessing.LabelEncoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelEncoder</span></code></a> can be used as
follows:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">preprocessing</span>
<span class="gp">>>> </span><span class="n">le</span> <span class="o">=</span> <span class="n">preprocessing</span><span class="o">.</span><span class="n">LabelEncoder</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">le</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">2</span><span class="p">,</span> <span class="mi">6</span><span class="p">])</span>
<span class="go">LabelEncoder()</span>
<span class="gp">>>> </span><span class="n">le</span><span class="o">.</span><span class="n">classes_</span>
<span class="go">array([1, 2, 6])</span>
<span class="gp">>>> </span><span class="n">le</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">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="go">array([0, 0, 1, 2])</span>
<span class="gp">>>> </span><span class="n">le</span><span class="o">.</span><span class="n">inverse_transform</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="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
<span class="go">array([1, 1, 2, 6])</span>
</pre></div>
</div>
<p>It can also be used to transform non-numerical labels (as long as they are
hashable and comparable) to numerical labels:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">le</span> <span class="o">=</span> <span class="n">preprocessing</span><span class="o">.</span><span class="n">LabelEncoder</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">le</span><span class="o">.</span><span class="n">fit</span><span class="p">([</span><span class="s2">"paris"</span><span class="p">,</span> <span class="s2">"paris"</span><span class="p">,</span> <span class="s2">"tokyo"</span><span class="p">,</span> <span class="s2">"amsterdam"</span><span class="p">])</span>
<span class="go">LabelEncoder()</span>
<span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">le</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
<span class="go">['amsterdam', 'paris', 'tokyo']</span>
<span class="gp">>>> </span><span class="n">le</span><span class="o">.</span><span class="n">transform</span><span class="p">([</span><span class="s2">"tokyo"</span><span class="p">,</span> <span class="s2">"tokyo"</span><span class="p">,</span> <span class="s2">"paris"</span><span class="p">])</span>
<span class="go">array([2, 2, 1])</span>
<span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">le</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">([</span><span class="mi">2</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">['tokyo', 'tokyo', 'paris']</span>
</pre></div>
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