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<li><a class="reference internal" href="#">4.8. Transforming the prediction target (<tt class="docutils literal"><span class="pre">y</span></tt>)</a><ul>
<li><a class="reference internal" href="#label-binarization">4.8.1. Label binarization</a></li>
<li><a class="reference internal" href="#label-encoding">4.8.2. Label encoding</a></li>
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<div class="section" id="transforming-the-prediction-target-y">
<span id="preprocessing-targets"></span><h1>4.8. Transforming the prediction target (<tt class="docutils literal"><span class="pre">y</span></tt>)<a class="headerlink" href="#transforming-the-prediction-target-y" title="Permalink to this headline">¶</a></h1>
<div class="section" id="label-binarization">
<h2>4.8.1. Label binarization<a class="headerlink" href="#label-binarization" title="Permalink to this headline">¶</a></h2>
<p><a class="reference internal" href="generated/sklearn.preprocessing.LabelBinarizer.html#sklearn.preprocessing.LabelBinarizer" title="sklearn.preprocessing.LabelBinarizer"><tt class="xref py py-class docutils literal"><span class="pre">LabelBinarizer</span></tt></a> is a utility class to help create a label indicator
matrix from a list of multi-class labels:</p>
<div class="highlight-python"><div class="highlight"><pre><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(neg_label=0, pos_label=1, sparse_output=False)</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>For multiple labels per instance, use <a class="reference internal" href="generated/sklearn.preprocessing.MultiLabelBinarizer.html#sklearn.preprocessing.MultiLabelBinarizer" title="sklearn.preprocessing.MultiLabelBinarizer"><tt class="xref py py-class docutils literal"><span class="pre">MultiLabelBinarizer</span></tt></a>:</p>
<div class="highlight-python"><div class="highlight"><pre><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">MultiLabelBinarizer</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="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="mi">3</span><span class="p">,)])</span>
<span class="go">array([[1, 1, 0],</span>
<span class="go"> [0, 0, 1]])</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, 3])</span>
</pre></div>
</div>
</div>
<div class="section" id="label-encoding">
<h2>4.8.2. Label encoding<a class="headerlink" href="#label-encoding" title="Permalink to this headline">¶</a></h2>
<p><a class="reference internal" href="generated/sklearn.preprocessing.LabelEncoder.html#sklearn.preprocessing.LabelEncoder" title="sklearn.preprocessing.LabelEncoder"><tt class="xref py py-class docutils literal"><span class="pre">LabelEncoder</span></tt></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"><tt class="xref py py-class docutils literal"><span class="pre">LabelEncoder</span></tt></a> can be used as
follows:</p>
<div class="highlight-python"><div class="highlight"><pre><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-python"><div class="highlight"><pre><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="s">"paris"</span><span class="p">,</span> <span class="s">"paris"</span><span class="p">,</span> <span class="s">"tokyo"</span><span class="p">,</span> <span class="s">"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="s">"tokyo"</span><span class="p">,</span> <span class="s">"tokyo"</span><span class="p">,</span> <span class="s">"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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