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<h1>ClassificationTree</h1>
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<section class="tex2jax_ignore mathjax_ignore" id="classificationtree">
<h1>ClassificationTree<a class="headerlink" href="#classificationtree" title="Permalink to this heading">#</a></h1>
<p>Link to Algorithm description: <a class="reference internal" href="../../dt.html"><span class="doc std std-doc">Decision Tree</span></a></p>
<dl class="py class">
<dt class="sig sig-object py" id="interpret.glassbox.ClassificationTree">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">interpret.glassbox.</span></span><span class="sig-name descname"><span class="pre">ClassificationTree</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">feature_names</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">feature_types</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">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interpret.glassbox.ClassificationTree" title="Permalink to this definition">#</a></dt>
<dd><p>Classification tree with shallow depth.</p>
<p>Initializes tree with low depth.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>feature_names</strong> – List of feature names.</p></li>
<li><p><strong>feature_types</strong> – List of feature types.</p></li>
<li><p><strong>max_depth</strong> – Max depth of tree.</p></li>
<li><p><strong>**kwargs</strong> – Kwargs sent to __init__() method of tree.</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="interpret.glassbox.ClassificationTree.explain_global">
<span class="sig-name descname"><span class="pre">explain_global</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</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="headerlink" href="#interpret.glassbox.ClassificationTree.explain_global" title="Permalink to this definition">#</a></dt>
<dd><p>Provides global explanation for model.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>name</strong> – User-defined explanation name.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>An explanation object,
visualizing feature-value pairs as horizontal bar chart.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="interpret.glassbox.ClassificationTree.explain_local">
<span class="sig-name descname"><span class="pre">explain_local</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="n"><span class="pre">name</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="headerlink" href="#interpret.glassbox.ClassificationTree.explain_local" title="Permalink to this definition">#</a></dt>
<dd><p>Provides local explanations for provided instances.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>X</strong> – Numpy array for X to explain.</p></li>
<li><p><strong>y</strong> – Numpy vector for y to explain.</p></li>
<li><p><strong>name</strong> – User-defined explanation name.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>An explanation object.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="interpret.glassbox.ClassificationTree.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></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</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">check_input</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="headerlink" href="#interpret.glassbox.ClassificationTree.fit" title="Permalink to this definition">#</a></dt>
<dd><p>Fits model to provided instances.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>X</strong> – Numpy array for training instances.</p></li>
<li><p><strong>y</strong> – Numpy array as training labels.</p></li>
<li><p><strong>sample_weight</strong> (<em>optional</em><em>[</em><em>np.ndarray</em><em>]</em>) – (n_samples,) Sample weights. If None (default), then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node.</p></li>
<li><p><strong>check_input</strong> (<em>bool</em>) – default=True. Allow to bypass several input checking. Don’t use this parameter unless you know what you’re doing.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>Itself.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="interpret.glassbox.ClassificationTree.predict">
<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interpret.glassbox.ClassificationTree.predict" title="Permalink to this definition">#</a></dt>
<dd><p>Predicts on provided instances.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>X</strong> – Numpy array for instances.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>Predicted class label per instance.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="interpret.glassbox.ClassificationTree.predict_proba">
<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interpret.glassbox.ClassificationTree.predict_proba" title="Permalink to this definition">#</a></dt>
<dd><p>Probability estimates on provided instances.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>X</strong> – Numpy array for instances.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>Probability estimate of instance for each class.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="interpret.glassbox.ClassificationTree.score">
<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</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="headerlink" href="#interpret.glassbox.ClassificationTree.score" title="Permalink to this definition">#</a></dt>
<dd><p>Return the mean accuracy on the given test data and labels.</p>
<p>In multi-label classification, this is the subset accuracy
which is a harsh metric since you require for each sample that
each label set be correctly predicted.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>X</strong> (<em>array-like of shape</em><em> (</em><em>n_samples</em><em>, </em><em>n_features</em><em>)</em>) – Test samples.</p></li>
<li><p><strong>y</strong> (<em>array-like of shape</em><em> (</em><em>n_samples</em><em>,</em><em>) or </em><em>(</em><em>n_samples</em><em>, </em><em>n_outputs</em><em>)</em>) – True labels for <cite>X</cite>.</p></li>
<li><p><strong>sample_weight</strong> (<em>array-like of shape</em><em> (</em><em>n_samples</em><em>,</em><em>)</em><em>, </em><em>default=None</em>) – Sample weights.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>score</strong> – Mean accuracy of <code class="docutils literal notranslate"><span class="pre">self.predict(X)</span></code> w.r.t. <cite>y</cite>.</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>float</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
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