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<div class="section" id="randomforestclassifier">
<span id="ranfor-api-doc"></span><h1>RandomForestClassifier<a class="headerlink" href="#randomforestclassifier" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="pai4sk.RandomForestClassifier">
<em class="property">class </em><code class="descclassname">pai4sk.</code><code class="descname">RandomForestClassifier</code><span class="sig-paren">(</span><em>n_estimators=10</em>, <em>criterion='gini'</em>, <em>max_depth=None</em>, <em>min_samples_leaf=1</em>, <em>max_features='auto'</em>, <em>bootstrap=True</em>, <em>n_jobs=None</em>, <em>random_state=None</em>, <em>verbose=False</em>, <em>use_gpu=False</em><span class="sig-paren">)</span><a class="headerlink" href="#pai4sk.RandomForestClassifier" title="Permalink to this definition">¶</a></dt>
<dd><p>Random Forest Classifier</p>
<p>This class implements a random forest classifier using the IBM Snap ML library.
It can be used for binary classification problems. It handles both dense and
sparse matrix inputs. Use csr, csc or ndarray matrix format for training and csr or
ndarray format for prediction.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>n_estimators</strong> (<em>integer</em><em>, </em><em>optional</em><em>, </em><em>default : 10</em>) – This parameter defines the number of trees in forest.</li>
<li><strong>criterion</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default : "gini"</em>) – This function measures the quality of a split. The currently supported criterion is “gini”.</li>
<li><strong>max_depth</strong> (<em>integer</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a><em>, </em><em>optional</em><em>, </em><em>default : None</em>) – The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than min_samples_leaf samples.</li>
<li><strong>min_samples_leaf</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a><em>, </em><em>optional</em><em>, </em><em>default : 1</em>) – <p>The minimum number of samples required to be at a leaf node.
A split point at any depth will only be considered if it leaves at
least <code class="docutils literal notranslate"><span class="pre">min_samples_leaf</span></code> training samples in each of the left and
right branches.
- If int, then consider <cite>min_samples_leaf</cite> as the minimum number.
- If float, then <cite>min_samples_leaf</cite> is a fraction and</p>
<blockquote>
<div><cite>ceil(min_samples_leaf * n_samples)</cite> are the minimum number of samples for each node.</div></blockquote>
</li>
<li><strong>max_features</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a><em>, </em><em>string</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a><em>, </em><em>optional</em><em>, </em><em>default : 'auto'</em>) – <dl class="docutils">
<dt>The number of features to consider when looking for the best split:</dt>
<dd><ul class="first last">
<li>If int, then consider <cite>max_features</cite> features at each split.</li>
<li>If float, then <cite>max_features</cite> is a fraction and
<cite>int(max_features * n_features)</cite> features are considered at each
split.</li>
<li>If “auto”, then <cite>max_features=sqrt(n_features)</cite>.</li>
<li>If “sqrt”, then <cite>max_features=sqrt(n_features)</cite>.</li>
<li>If “log2”, then <cite>max_features=log2(n_features)</cite>.</li>
<li>If None, then <cite>max_features=n_features</cite>.</li>
</ul>
</dd>
</dl>
</li>
<li><strong>bootstrap</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default : True</em>) – This parameter determines whether bootstrap samples are used when building trees.</li>
<li><strong>n_jobs</strong> (<em>integer</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a><em>, </em><em>optional</em><em>, </em><em>default : None</em>) – The number of jobs to run in parallel the fit function. None = 1 process.</li>
<li><strong>random_state</strong> (<em>integer</em><em>, or </em><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a><em>, </em><em>optional</em><em>, </em><em>default : None</em>) – If integer, random_state is the seed used by the random number generator.
If None, the random number generator is the RandomState instance used by <cite>np.random</cite>.</li>
<li><strong>verbose</strong> (<em>boolean</em><em>, </em><em>default : False</em>) – If True, it prints debugging information while training.
Warning: this will increase the training time. For performance evaluation, use verbose=False.</li>
<li><strong>use_gpu</strong> (<em>boolean</em><em>, </em><em>default : False</em>) – Flag that indicates the hardware platform used for training. If True, the training
is performed using the GPU. If False, the training is performed using the CPU. Currently
only CPU training is supported.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="pai4sk.RandomForestClassifier.fit">
<code class="descname">fit</code><span class="sig-paren">(</span><em>X_train</em>, <em>y_train</em><span class="sig-paren">)</span><a class="headerlink" href="#pai4sk.RandomForestClassifier.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit the model according to the given train data.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>X_train</strong> (<em>sparse matrix</em><em> (</em><em>csr_matrix</em><em>, </em><em>csc_matrix</em><em>) or </em><em>dense matrix</em><em> (</em><em>ndarray</em><em>)</em>) – Train dataset</li>
<li><strong>y_train</strong> (<em>array-like</em><em>, </em><em>shape =</em><em> (</em><em>n_samples</em><em>,</em><em>)</em>) – The target vector corresponding to X_train.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>self</strong></p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.7)">object</a></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pai4sk.RandomForestClassifier.get_params">
<code class="descname">get_params</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pai4sk.RandomForestClassifier.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get the values of the model parameters.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>params</strong></td>
</tr>
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.7)">dict</a></td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pai4sk.RandomForestClassifier.predict">
<code class="descname">predict</code><span class="sig-paren">(</span><em>X</em>, <em>num_threads=0</em><span class="sig-paren">)</span><a class="headerlink" href="#pai4sk.RandomForestClassifier.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Class predictions</p>
<p>The returned class estimates.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>X</strong> (<em>sparse matrix</em><em> (</em><em>csr_matrix</em><em>) or </em><em>dense matrix</em><em> (</em><em>ndarray</em><em>)</em>) – Dataset used for predicting class estimates.</li>
<li><strong>num_threads</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em>, </em><em>default : 0</em>) – Number of threads used to run inference.
By default inference runs with maximum number of available threads.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>proba</strong> – Returns the predicted class of the sample.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">array-like, shape = (n_samples,)</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pai4sk.RandomForestClassifier.predict_log_proba">
<code class="descname">predict_log_proba</code><span class="sig-paren">(</span><em>X</em>, <em>num_threads=0</em><span class="sig-paren">)</span><a class="headerlink" href="#pai4sk.RandomForestClassifier.predict_log_proba" title="Permalink to this definition">¶</a></dt>
<dd><p>Log of probability estimates</p>
<p>The returned log-probability estimates for the two classes.
Only for binary classification.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>X</strong> (<em>sparse matrix</em><em> (</em><em>csr_matrix</em><em>) or </em><em>dense matrix</em><em> (</em><em>ndarray</em><em>)</em>) – Dataset used for predicting log-probability estimates.</li>
<li><strong>num_threads</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em>, </em><em>default : 0</em>) – Number of threads used to run inference.
By default inference runs with maximum number of available threads.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"></p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.7)">None</a></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pai4sk.RandomForestClassifier.predict_proba">
<code class="descname">predict_proba</code><span class="sig-paren">(</span><em>X</em>, <em>num_threads=0</em><span class="sig-paren">)</span><a class="headerlink" href="#pai4sk.RandomForestClassifier.predict_proba" title="Permalink to this definition">¶</a></dt>
<dd><p>Probability estimates</p>
<p>The returned probability estimates for the two classes.
Only for binary classification.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>X</strong> (<em>sparse matrix</em><em> (</em><em>csr_matrix</em><em>) or </em><em>dense matrix</em><em> (</em><em>ndarray</em><em>)</em>) – Dataset used for predicting probability estimates.</li>
<li><strong>num_threads</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em>, </em><em>default : 0</em>) – Number of threads used to run inference.
By default inference runs with maximum number of available threads.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"></p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.7)">None</a></p>
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