Added 1.5.0 docs.

author: Reynold Xin <rxin@apache.org> 2015-09-08 23:20:31 +0000
committer: Reynold Xin <rxin@apache.org> 2015-09-08 23:20:31 +0000
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+            <h1 class="title"><a href="ml-guide.html">ML</a> - Decision Trees</h1>
+          
+
+          <p><strong>Table of Contents</strong></p>
+
+<ul id="markdown-toc">
+  <li><a href="#overview">Overview</a></li>
+  <li><a href="#inputs-and-outputs">Inputs and Outputs</a>    <ul>
+      <li><a href="#input-columns">Input Columns</a></li>
+      <li><a href="#output-columns">Output Columns</a></li>
+    </ul>
+  </li>
+  <li><a href="#examples">Examples</a>    <ul>
+      <li><a href="#classification">Classification</a></li>
+      <li><a href="#regression">Regression</a></li>
+    </ul>
+  </li>
+</ul>
+
+<h1 id="overview">Overview</h1>
+
+<p><a href="http://en.wikipedia.org/wiki/Decision_tree_learning">Decision trees</a>
+and their ensembles are popular methods for the machine learning tasks of
+classification and regression. Decision trees are widely used since they are easy to interpret,
+handle categorical features, extend to the multiclass classification setting, do not require
+feature scaling, and are able to capture non-linearities and feature interactions. Tree ensemble
+algorithms such as random forests and boosting are among the top performers for classification and
+regression tasks.</p>
+
+<p>MLlib supports decision trees for binary and multiclass classification and for regression,
+using both continuous and categorical features. The implementation partitions data by rows,
+allowing distributed training with millions or even billions of instances.</p>
+
+<p>Users can find more information about the decision tree algorithm in the <a href="mllib-decision-tree.html">MLlib Decision Tree guide</a>.  In this section, we demonstrate the Pipelines API for Decision Trees.</p>
+
+<p>The Pipelines API for Decision Trees offers a bit more functionality than the original API.  In particular, for classification, users can get the predicted probability of each class (a.k.a. class conditional probabilities).</p>
+
+<p>Ensembles of trees (Random Forests and Gradient-Boosted Trees) are described in the <a href="ml-ensembles.html">Ensembles guide</a>.</p>
+
+<h1 id="inputs-and-outputs">Inputs and Outputs</h1>
+
+<p>We list the input and output (prediction) column types here.
+All output columns are optional; to exclude an output column, set its corresponding Param to an empty string.</p>
+
+<h2 id="input-columns">Input Columns</h2>
+
+<table class="table">
+  <thead>
+    <tr>
+      <th align="left">Param name</th>
+      <th align="left">Type(s)</th>
+      <th align="left">Default</th>
+      <th align="left">Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>labelCol</td>
+      <td>Double</td>
+      <td>"label"</td>
+      <td>Label to predict</td>
+    </tr>
+    <tr>
+      <td>featuresCol</td>
+      <td>Vector</td>
+      <td>"features"</td>
+      <td>Feature vector</td>
+    </tr>
+  </tbody>
+</table>
+
+<h2 id="output-columns">Output Columns</h2>
+
+<table class="table">
+  <thead>
+    <tr>
+      <th align="left">Param name</th>
+      <th align="left">Type(s)</th>
+      <th align="left">Default</th>
+      <th align="left">Description</th>
+      <th align="left">Notes</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>predictionCol</td>
+      <td>Double</td>
+      <td>"prediction"</td>
+      <td>Predicted label</td>
+      <td></td>
+    </tr>
+    <tr>
+      <td>rawPredictionCol</td>
+      <td>Vector</td>
+      <td>"rawPrediction"</td>
+      <td>Vector of length # classes, with the counts of training instance labels at the tree node which makes the prediction</td>
+      <td>Classification only</td>
+    </tr>
+    <tr>
+      <td>probabilityCol</td>
+      <td>Vector</td>
+      <td>"probability"</td>
+      <td>Vector of length # classes equal to rawPrediction normalized to a multinomial distribution</td>
+      <td>Classification only</td>
+    </tr>
+  </tbody>
+</table>
+
+<h1 id="examples">Examples</h1>
+
+<p>The below examples demonstrate the Pipelines API for Decision Trees. The main differences between this API and the <a href="mllib-decision-tree.html">original MLlib Decision Tree API</a> are:</p>
+
+<ul>
+  <li>support for ML Pipelines</li>
+  <li>separation of Decision Trees for classification vs. regression</li>
+  <li>use of DataFrame metadata to distinguish continuous and categorical features</li>
+</ul>
+
+<h2 id="classification">Classification</h2>
+
+<p>The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set.
+We use two feature transformers to prepare the data; these help index categories for the label and categorical features, adding metadata to the <code>DataFrame</code> which the Decision Tree algorithm can recognize.</p>
+
+<div class="codetabs">
+<div data-lang="scala">
+
+    <p>More details on parameters can be found in the <a href="api/scala/index.html#org.apache.spark.ml.classification.DecisionTreeClassifier">Scala API documentation</a>.</p>
+
+    <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span>
+<span class="k">import</span> <span class="nn">org.apache.spark.ml.classification.DecisionTreeClassifier</span>
+<span class="k">import</span> <span class="nn">org.apache.spark.ml.classification.DecisionTreeClassificationModel</span>
+<span class="k">import</span> <span class="nn">org.apache.spark.ml.feature.</span><span class="o">{</span><span class="nc">StringIndexer</span><span class="o">,</span> <span class="nc">IndexToString</span><span class="o">,</span> <span class="nc">VectorIndexer</span><span class="o">}</span>
+<span class="k">import</span> <span class="nn">org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator</span>
+<span class="k">import</span> <span class="nn">org.apache.spark.mllib.util.MLUtils</span>
+
+<span class="c1">// Load and parse the data file, converting it to a DataFrame.</span>
+<span class="k">val</span> <span class="n">data</span> <span class="k">=</span> <span class="nc">MLUtils</span><span class="o">.</span><span class="n">loadLibSVMFile</span><span class="o">(</span><span class="n">sc</span><span class="o">,</span> <span class="s">&quot;data/mllib/sample_libsvm_data.txt&quot;</span><span class="o">).</span><span class="n">toDF</span><span class="o">()</span>
+
+<span class="c1">// Index labels, adding metadata to the label column.</span>
+<span class="c1">// Fit on whole dataset to include all labels in index.</span>
+<span class="k">val</span> <span class="n">labelIndexer</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">StringIndexer</span><span class="o">()</span>
+  <span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="s">&quot;label&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">&quot;indexedLabel&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">data</span><span class="o">)</span>
+<span class="c1">// Automatically identify categorical features, and index them.</span>
+<span class="k">val</span> <span class="n">featureIndexer</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">VectorIndexer</span><span class="o">()</span>
+  <span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="s">&quot;features&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">&quot;indexedFeatures&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="n">setMaxCategories</span><span class="o">(</span><span class="mi">4</span><span class="o">)</span> <span class="c1">// features with &gt; 4 distinct values are treated as continuous</span>
+  <span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">data</span><span class="o">)</span>
+
+<span class="c1">// Split the data into training and test sets (30% held out for testing)</span>
+<span class="k">val</span> <span class="nc">Array</span><span class="o">(</span><span class="n">trainingData</span><span class="o">,</span> <span class="n">testData</span><span class="o">)</span> <span class="k">=</span> <span class="n">data</span><span class="o">.</span><span class="n">randomSplit</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="mf">0.7</span><span class="o">,</span> <span class="mf">0.3</span><span class="o">))</span>
+
+<span class="c1">// Train a DecisionTree model.</span>
+<span class="k">val</span> <span class="n">dt</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">DecisionTreeClassifier</span><span class="o">()</span>
+  <span class="o">.</span><span class="n">setLabelCol</span><span class="o">(</span><span class="s">&quot;indexedLabel&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="n">setFeaturesCol</span><span class="o">(</span><span class="s">&quot;indexedFeatures&quot;</span><span class="o">)</span>
+
+<span class="c1">// Convert indexed labels back to original labels.</span>
+<span class="k">val</span> <span class="n">labelConverter</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">IndexToString</span><span class="o">()</span>
+  <span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="s">&quot;prediction&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">&quot;predictedLabel&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="n">setLabels</span><span class="o">(</span><span class="n">labelIndexer</span><span class="o">.</span><span class="n">labels</span><span class="o">)</span>
+
+<span class="c1">// Chain indexers and tree in a Pipeline</span>
+<span class="k">val</span> <span class="n">pipeline</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">Pipeline</span><span class="o">()</span>
+  <span class="o">.</span><span class="n">setStages</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="n">labelIndexer</span><span class="o">,</span> <span class="n">featureIndexer</span><span class="o">,</span> <span class="n">dt</span><span class="o">,</span> <span class="n">labelConverter</span><span class="o">))</span>
+
+<span class="c1">// Train model.  This also runs the indexers.</span>
+<span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">trainingData</span><span class="o">)</span>
+
+<span class="c1">// Make predictions.</span>
+<span class="k">val</span> <span class="n">predictions</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="o">(</span><span class="n">testData</span><span class="o">)</span>
+
+<span class="c1">// Select example rows to display.</span>
+<span class="n">predictions</span><span class="o">.</span><span class="n">select</span><span class="o">(</span><span class="s">&quot;predictedLabel&quot;</span><span class="o">,</span> <span class="s">&quot;label&quot;</span><span class="o">,</span> <span class="s">&quot;features&quot;</span><span class="o">).</span><span class="n">show</span><span class="o">(</span><span class="mi">5</span><span class="o">)</span>
+
+<span class="c1">// Select (prediction, true label) and compute test error</span>
+<span class="k">val</span> <span class="n">evaluator</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">MulticlassClassificationEvaluator</span><span class="o">()</span>
+  <span class="o">.</span><span class="n">setLabelCol</span><span class="o">(</span><span class="s">&quot;indexedLabel&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="n">setPredictionCol</span><span class="o">(</span><span class="s">&quot;prediction&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="n">setMetricName</span><span class="o">(</span><span class="s">&quot;precision&quot;</span><span class="o">)</span>
+<span class="k">val</span> <span class="n">accuracy</span> <span class="k">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="n">evaluate</span><span class="o">(</span><span class="n">predictions</span><span class="o">)</span>
+<span class="n">println</span><span class="o">(</span><span class="s">&quot;Test Error = &quot;</span> <span class="o">+</span> <span class="o">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">accuracy</span><span class="o">))</span>
+
+<span class="k">val</span> <span class="n">treeModel</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">stages</span><span class="o">(</span><span class="mi">2</span><span class="o">).</span><span class="n">asInstanceOf</span><span class="o">[</span><span class="kt">DecisionTreeClassificationModel</span><span class="o">]</span>
+<span class="n">println</span><span class="o">(</span><span class="s">&quot;Learned classification tree model:\n&quot;</span> <span class="o">+</span> <span class="n">treeModel</span><span class="o">.</span><span class="n">toDebugString</span><span class="o">)</span></code></pre></div>
+
+  </div>
+
+<div data-lang="java">
+
+    <p>More details on parameters can be found in the <a href="api/java/org/apache/spark/ml/classification/DecisionTreeClassifier.html">Java API documentation</a>.</p>
+
+    <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span><span class="o">;</span>
+<span class="kn">import</span> <span class="nn">org.apache.spark.ml.PipelineModel</span><span class="o">;</span>
+<span class="kn">import</span> <span class="nn">org.apache.spark.ml.PipelineStage</span><span class="o">;</span>
+<span class="kn">import</span> <span class="nn">org.apache.spark.ml.classification.DecisionTreeClassifier</span><span class="o">;</span>
+<span class="kn">import</span> <span class="nn">org.apache.spark.ml.classification.DecisionTreeClassificationModel</span><span class="o">;</span>
+<span class="kn">import</span> <span class="nn">org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator</span><span class="o">;</span>
+<span class="kn">import</span> <span class="nn">org.apache.spark.ml.feature.*</span><span class="o">;</span>
+<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.regression.LabeledPoint</span><span class="o">;</span>
+<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.util.MLUtils</span><span class="o">;</span>
+<span class="kn">import</span> <span class="nn">org.apache.spark.rdd.RDD</span><span class="o">;</span>
+<span class="kn">import</span> <span class="nn">org.apache.spark.sql.DataFrame</span><span class="o">;</span>
+
+<span class="c1">// Load and parse the data file, converting it to a DataFrame.</span>
+<span class="n">RDD</span><span class="o">&lt;</span><span class="n">LabeledPoint</span><span class="o">&gt;</span> <span class="n">rdd</span> <span class="o">=</span> <span class="n">MLUtils</span><span class="o">.</span><span class="na">loadLibSVMFile</span><span class="o">(</span><span class="n">sc</span><span class="o">.</span><span class="na">sc</span><span class="o">(),</span> <span class="s">&quot;data/mllib/sample_libsvm_data.txt&quot;</span><span class="o">);</span>
+<span class="n">DataFrame</span> <span class="n">data</span> <span class="o">=</span> <span class="n">jsql</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">rdd</span><span class="o">,</span> <span class="n">LabeledPoint</span><span class="o">.</span><span class="na">class</span><span class="o">);</span>
+
+<span class="c1">// Index labels, adding metadata to the label column.</span>
+<span class="c1">// Fit on whole dataset to include all labels in index.</span>
+<span class="n">StringIndexerModel</span> <span class="n">labelIndexer</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">StringIndexer</span><span class="o">()</span>
+  <span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="s">&quot;label&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">&quot;indexedLabel&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">data</span><span class="o">);</span>
+<span class="c1">// Automatically identify categorical features, and index them.</span>
+<span class="n">VectorIndexerModel</span> <span class="n">featureIndexer</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">VectorIndexer</span><span class="o">()</span>
+  <span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="s">&quot;features&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">&quot;indexedFeatures&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="na">setMaxCategories</span><span class="o">(</span><span class="mi">4</span><span class="o">)</span> <span class="c1">// features with &gt; 4 distinct values are treated as continuous</span>
+  <span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">data</span><span class="o">);</span>
+
+<span class="c1">// Split the data into training and test sets (30% held out for testing)</span>
+<span class="n">DataFrame</span><span class="o">[]</span> <span class="n">splits</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="na">randomSplit</span><span class="o">(</span><span class="k">new</span> <span class="kt">double</span><span class="o">[]</span> <span class="o">{</span><span class="mf">0.7</span><span class="o">,</span> <span class="mf">0.3</span><span class="o">});</span>
+<span class="n">DataFrame</span> <span class="n">trainingData</span> <span class="o">=</span> <span class="n">splits</span><span class="o">[</span><span class="mi">0</span><span class="o">];</span>
+<span class="n">DataFrame</span> <span class="n">testData</span> <span class="o">=</span> <span class="n">splits</span><span class="o">[</span><span class="mi">1</span><span class="o">];</span>
+
+<span class="c1">// Train a DecisionTree model.</span>
+<span class="n">DecisionTreeClassifier</span> <span class="n">dt</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">DecisionTreeClassifier</span><span class="o">()</span>
+  <span class="o">.</span><span class="na">setLabelCol</span><span class="o">(</span><span class="s">&quot;indexedLabel&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="na">setFeaturesCol</span><span class="o">(</span><span class="s">&quot;indexedFeatures&quot;</span><span class="o">);</span>
+
+<span class="c1">// Convert indexed labels back to original labels.</span>
+<span class="n">IndexToString</span> <span class="n">labelConverter</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">IndexToString</span><span class="o">()</span>
+  <span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="s">&quot;prediction&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">&quot;predictedLabel&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="na">setLabels</span><span class="o">(</span><span class="n">labelIndexer</span><span class="o">.</span><span class="na">labels</span><span class="o">());</span>
+
+<span class="c1">// Chain indexers and tree in a Pipeline</span>
+<span class="n">Pipeline</span> <span class="n">pipeline</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">Pipeline</span><span class="o">()</span>
+  <span class="o">.</span><span class="na">setStages</span><span class="o">(</span><span class="k">new</span> <span class="n">PipelineStage</span><span class="o">[]</span> <span class="o">{</span><span class="n">labelIndexer</span><span class="o">,</span> <span class="n">featureIndexer</span><span class="o">,</span> <span class="n">dt</span><span class="o">,</span> <span class="n">labelConverter</span><span class="o">});</span>
+
+<span class="c1">// Train model.  This also runs the indexers.</span>
+<span class="n">PipelineModel</span> <span class="n">model</span> <span class="o">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">trainingData</span><span class="o">);</span>
+
+<span class="c1">// Make predictions.</span>
+<span class="n">DataFrame</span> <span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="na">transform</span><span class="o">(</span><span class="n">testData</span><span class="o">);</span>
+
+<span class="c1">// Select example rows to display.</span>
+<span class="n">predictions</span><span class="o">.</span><span class="na">select</span><span class="o">(</span><span class="s">&quot;predictedLabel&quot;</span><span class="o">,</span> <span class="s">&quot;label&quot;</span><span class="o">,</span> <span class="s">&quot;features&quot;</span><span class="o">).</span><span class="na">show</span><span class="o">(</span><span class="mi">5</span><span class="o">);</span>
+
+<span class="c1">// Select (prediction, true label) and compute test error</span>
+<span class="n">MulticlassClassificationEvaluator</span> <span class="n">evaluator</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">MulticlassClassificationEvaluator</span><span class="o">()</span>
+  <span class="o">.</span><span class="na">setLabelCol</span><span class="o">(</span><span class="s">&quot;indexedLabel&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="na">setPredictionCol</span><span class="o">(</span><span class="s">&quot;prediction&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="na">setMetricName</span><span class="o">(</span><span class="s">&quot;precision&quot;</span><span class="o">);</span>
+<span class="kt">double</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="na">evaluate</span><span class="o">(</span><span class="n">predictions</span><span class="o">);</span>
+<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">&quot;Test Error = &quot;</span> <span class="o">+</span> <span class="o">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">accuracy</span><span class="o">));</span>
+
+<span class="n">DecisionTreeClassificationModel</span> <span class="n">treeModel</span> <span class="o">=</span>
+  <span class="o">(</span><span class="n">DecisionTreeClassificationModel</span><span class="o">)(</span><span class="n">model</span><span class="o">.</span><span class="na">stages</span><span class="o">()[</span><span class="mi">2</span><span class="o">]);</span>
+<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">&quot;Learned classification tree model:\n&quot;</span> <span class="o">+</span> <span class="n">treeModel</span><span class="o">.</span><span class="na">toDebugString</span><span class="o">());</span></code></pre></div>
+
+  </div>
+
+<div data-lang="python">
+
+    <p>More details on parameters can be found in the <a href="api/python/pyspark.ml.html#pyspark.ml.classification.DecisionTreeClassifier">Python API documentation</a>.</p>
+
+    <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">pyspark.ml</span> <span class="kn">import</span> <span class="n">Pipeline</span>
+<span class="kn">from</span> <span class="nn">pyspark.ml.classification</span> <span class="kn">import</span> <span class="n">DecisionTreeClassifier</span>
+<span class="kn">from</span> <span class="nn">pyspark.ml.feature</span> <span class="kn">import</span> <span class="n">StringIndexer</span><span class="p">,</span> <span class="n">VectorIndexer</span>
+<span class="kn">from</span> <span class="nn">pyspark.ml.evaluation</span> <span class="kn">import</span> <span class="n">MulticlassClassificationEvaluator</span>
+<span class="kn">from</span> <span class="nn">pyspark.mllib.util</span> <span class="kn">import</span> <span class="n">MLUtils</span>
+
+<span class="c"># Load and parse the data file, converting it to a DataFrame.</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">MLUtils</span><span class="o">.</span><span class="n">loadLibSVMFile</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="s">&quot;data/mllib/sample_libsvm_data.txt&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">toDF</span><span class="p">()</span>
+
+<span class="c"># Index labels, adding metadata to the label column.</span>
+<span class="c"># Fit on whole dataset to include all labels in index.</span>
+<span class="n">labelIndexer</span> <span class="o">=</span> <span class="n">StringIndexer</span><span class="p">(</span><span class="n">inputCol</span><span class="o">=</span><span class="s">&quot;label&quot;</span><span class="p">,</span> <span class="n">outputCol</span><span class="o">=</span><span class="s">&quot;indexedLabel&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="c"># Automatically identify categorical features, and index them.</span>
+<span class="c"># We specify maxCategories so features with &gt; 4 distinct values are treated as continuous.</span>
+<span class="n">featureIndexer</span> <span class="o">=</span>\
+    <span class="n">VectorIndexer</span><span class="p">(</span><span class="n">inputCol</span><span class="o">=</span><span class="s">&quot;features&quot;</span><span class="p">,</span> <span class="n">outputCol</span><span class="o">=</span><span class="s">&quot;indexedFeatures&quot;</span><span class="p">,</span> <span class="n">maxCategories</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+<span class="c"># Split the data into training and test sets (30% held out for testing)</span>
+<span class="p">(</span><span class="n">trainingData</span><span class="p">,</span> <span class="n">testData</span><span class="p">)</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">randomSplit</span><span class="p">([</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">])</span>
+
+<span class="c"># Train a DecisionTree model.</span>
+<span class="n">dt</span> <span class="o">=</span> <span class="n">DecisionTreeClassifier</span><span class="p">(</span><span class="n">labelCol</span><span class="o">=</span><span class="s">&quot;indexedLabel&quot;</span><span class="p">,</span> <span class="n">featuresCol</span><span class="o">=</span><span class="s">&quot;indexedFeatures&quot;</span><span class="p">)</span>
+
+<span class="c"># Chain indexers and tree in a Pipeline</span>
+<span class="n">pipeline</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span><span class="n">stages</span><span class="o">=</span><span class="p">[</span><span class="n">labelIndexer</span><span class="p">,</span> <span class="n">featureIndexer</span><span class="p">,</span> <span class="n">dt</span><span class="p">])</span>
+
+<span class="c"># Train model.  This also runs the indexers.</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">trainingData</span><span class="p">)</span>
+
+<span class="c"># Make predictions.</span>
+<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">testData</span><span class="p">)</span>
+
+<span class="c"># Select example rows to display.</span>
+<span class="n">predictions</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s">&quot;prediction&quot;</span><span class="p">,</span> <span class="s">&quot;indexedLabel&quot;</span><span class="p">,</span> <span class="s">&quot;features&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+
+<span class="c"># Select (prediction, true label) and compute test error</span>
+<span class="n">evaluator</span> <span class="o">=</span> <span class="n">MulticlassClassificationEvaluator</span><span class="p">(</span>
+    <span class="n">labelCol</span><span class="o">=</span><span class="s">&quot;indexedLabel&quot;</span><span class="p">,</span> <span class="n">predictionCol</span><span class="o">=</span><span class="s">&quot;prediction&quot;</span><span class="p">,</span> <span class="n">metricName</span><span class="o">=</span><span class="s">&quot;precision&quot;</span><span class="p">)</span>
+<span class="n">accuracy</span> <span class="o">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span>
+<span class="k">print</span> <span class="s">&quot;Test Error = </span><span class="si">%g</span><span class="s">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">accuracy</span><span class="p">)</span>
+
+<span class="n">treeModel</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">stages</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+<span class="k">print</span> <span class="n">treeModel</span> <span class="c"># summary only</span></code></pre></div>
+
+  </div>
+
+</div>
+
+<h2 id="regression">Regression</h2>
+
+<p>The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set.
+We use a feature transformer to index categorical features, adding metadata to the <code>DataFrame</code> which the Decision Tree algorithm can recognize.</p>
+
+<div class="codetabs">
+<div data-lang="scala">
+
+    <p>More details on parameters can be found in the <a href="api/scala/index.html#org.apache.spark.ml.regression.DecisionTreeRegressor">Scala API documentation</a>.</p>
+
+    <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span>
+<span class="k">import</span> <span class="nn">org.apache.spark.ml.regression.DecisionTreeRegressor</span>
+<span class="k">import</span> <span class="nn">org.apache.spark.ml.regression.DecisionTreeRegressionModel</span>
+<span class="k">import</span> <span class="nn">org.apache.spark.ml.feature.VectorIndexer</span>
+<span class="k">import</span> <span class="nn">org.apache.spark.ml.evaluation.RegressionEvaluator</span>
+<span class="k">import</span> <span class="nn">org.apache.spark.mllib.util.MLUtils</span>
+
+<span class="c1">// Load and parse the data file, converting it to a DataFrame.</span>
+<span class="k">val</span> <span class="n">data</span> <span class="k">=</span> <span class="nc">MLUtils</span><span class="o">.</span><span class="n">loadLibSVMFile</span><span class="o">(</span><span class="n">sc</span><span class="o">,</span> <span class="s">&quot;data/mllib/sample_libsvm_data.txt&quot;</span><span class="o">).</span><span class="n">toDF</span><span class="o">()</span>
+
+<span class="c1">// Automatically identify categorical features, and index them.</span>
+<span class="c1">// Here, we treat features with &gt; 4 distinct values as continuous.</span>
+<span class="k">val</span> <span class="n">featureIndexer</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">VectorIndexer</span><span class="o">()</span>
+  <span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="s">&quot;features&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">&quot;indexedFeatures&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="n">setMaxCategories</span><span class="o">(</span><span class="mi">4</span><span class="o">)</span>
+  <span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">data</span><span class="o">)</span>
+
+<span class="c1">// Split the data into training and test sets (30% held out for testing)</span>
+<span class="k">val</span> <span class="nc">Array</span><span class="o">(</span><span class="n">trainingData</span><span class="o">,</span> <span class="n">testData</span><span class="o">)</span> <span class="k">=</span> <span class="n">data</span><span class="o">.</span><span class="n">randomSplit</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="mf">0.7</span><span class="o">,</span> <span class="mf">0.3</span><span class="o">))</span>
+
+<span class="c1">// Train a DecisionTree model.</span>
+<span class="k">val</span> <span class="n">dt</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">DecisionTreeRegressor</span><span class="o">()</span>
+  <span class="o">.</span><span class="n">setLabelCol</span><span class="o">(</span><span class="s">&quot;label&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="n">setFeaturesCol</span><span class="o">(</span><span class="s">&quot;indexedFeatures&quot;</span><span class="o">)</span>
+
+<span class="c1">// Chain indexer and tree in a Pipeline</span>
+<span class="k">val</span> <span class="n">pipeline</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">Pipeline</span><span class="o">()</span>
+  <span class="o">.</span><span class="n">setStages</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="n">featureIndexer</span><span class="o">,</span> <span class="n">dt</span><span class="o">))</span>
+
+<span class="c1">// Train model.  This also runs the indexer.</span>
+<span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">trainingData</span><span class="o">)</span>
+
+<span class="c1">// Make predictions.</span>
+<span class="k">val</span> <span class="n">predictions</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="o">(</span><span class="n">testData</span><span class="o">)</span>
+
+<span class="c1">// Select example rows to display.</span>
+<span class="n">predictions</span><span class="o">.</span><span class="n">select</span><span class="o">(</span><span class="s">&quot;prediction&quot;</span><span class="o">,</span> <span class="s">&quot;label&quot;</span><span class="o">,</span> <span class="s">&quot;features&quot;</span><span class="o">).</span><span class="n">show</span><span class="o">(</span><span class="mi">5</span><span class="o">)</span>
+
+<span class="c1">// Select (prediction, true label) and compute test error</span>
+<span class="k">val</span> <span class="n">evaluator</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">RegressionEvaluator</span><span class="o">()</span>
+  <span class="o">.</span><span class="n">setLabelCol</span><span class="o">(</span><span class="s">&quot;label&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="n">setPredictionCol</span><span class="o">(</span><span class="s">&quot;prediction&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="n">setMetricName</span><span class="o">(</span><span class="s">&quot;rmse&quot;</span><span class="o">)</span>
+<span class="k">val</span> <span class="n">rmse</span> <span class="k">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="n">evaluate</span><span class="o">(</span><span class="n">predictions</span><span class="o">)</span>
+<span class="n">println</span><span class="o">(</span><span class="s">&quot;Root Mean Squared Error (RMSE) on test data = &quot;</span> <span class="o">+</span> <span class="n">rmse</span><span class="o">)</span>
+
+<span class="k">val</span> <span class="n">treeModel</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">stages</span><span class="o">(</span><span class="mi">1</span><span class="o">).</span><span class="n">asInstanceOf</span><span class="o">[</span><span class="kt">DecisionTreeRegressionModel</span><span class="o">]</span>
+<span class="n">println</span><span class="o">(</span><span class="s">&quot;Learned regression tree model:\n&quot;</span> <span class="o">+</span> <span class="n">treeModel</span><span class="o">.</span><span class="n">toDebugString</span><span class="o">)</span></code></pre></div>
+
+  </div>
+
+<div data-lang="java">
+
+    <p>More details on parameters can be found in the <a href="api/java/org/apache/spark/ml/regression/DecisionTreeRegressor.html">Java API documentation</a>.</p>
+
+    <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span><span class="o">;</span>
+<span class="kn">import</span> <span class="nn">org.apache.spark.ml.PipelineModel</span><span class="o">;</span>
+<span class="kn">import</span> <span class="nn">org.apache.spark.ml.PipelineStage</span><span class="o">;</span>
+<span class="kn">import</span> <span class="nn">org.apache.spark.ml.evaluation.RegressionEvaluator</span><span class="o">;</span>
+<span class="kn">import</span> <span class="nn">org.apache.spark.ml.feature.VectorIndexer</span><span class="o">;</span>
+<span class="kn">import</span> <span class="nn">org.apache.spark.ml.feature.VectorIndexerModel</span><span class="o">;</span>
+<span class="kn">import</span> <span class="nn">org.apache.spark.ml.regression.DecisionTreeRegressionModel</span><span class="o">;</span>
+<span class="kn">import</span> <span class="nn">org.apache.spark.ml.regression.DecisionTreeRegressor</span><span class="o">;</span>
+<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.regression.LabeledPoint</span><span class="o">;</span>
+<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.util.MLUtils</span><span class="o">;</span>
+<span class="kn">import</span> <span class="nn">org.apache.spark.rdd.RDD</span><span class="o">;</span>
+<span class="kn">import</span> <span class="nn">org.apache.spark.sql.DataFrame</span><span class="o">;</span>
+
+<span class="c1">// Load and parse the data file, converting it to a DataFrame.</span>
+<span class="n">RDD</span><span class="o">&lt;</span><span class="n">LabeledPoint</span><span class="o">&gt;</span> <span class="n">rdd</span> <span class="o">=</span> <span class="n">MLUtils</span><span class="o">.</span><span class="na">loadLibSVMFile</span><span class="o">(</span><span class="n">sc</span><span class="o">.</span><span class="na">sc</span><span class="o">(),</span> <span class="s">&quot;data/mllib/sample_libsvm_data.txt&quot;</span><span class="o">);</span>
+<span class="n">DataFrame</span> <span class="n">data</span> <span class="o">=</span> <span class="n">jsql</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">rdd</span><span class="o">,</span> <span class="n">LabeledPoint</span><span class="o">.</span><span class="na">class</span><span class="o">);</span>
+
+<span class="c1">// Automatically identify categorical features, and index them.</span>
+<span class="c1">// Set maxCategories so features with &gt; 4 distinct values are treated as continuous.</span>
+<span class="n">VectorIndexerModel</span> <span class="n">featureIndexer</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">VectorIndexer</span><span class="o">()</span>
+  <span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="s">&quot;features&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">&quot;indexedFeatures&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="na">setMaxCategories</span><span class="o">(</span><span class="mi">4</span><span class="o">)</span>
+  <span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">data</span><span class="o">);</span>
+
+<span class="c1">// Split the data into training and test sets (30% held out for testing)</span>
+<span class="n">DataFrame</span><span class="o">[]</span> <span class="n">splits</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="na">randomSplit</span><span class="o">(</span><span class="k">new</span> <span class="kt">double</span><span class="o">[]</span> <span class="o">{</span><span class="mf">0.7</span><span class="o">,</span> <span class="mf">0.3</span><span class="o">});</span>
+<span class="n">DataFrame</span> <span class="n">trainingData</span> <span class="o">=</span> <span class="n">splits</span><span class="o">[</span><span class="mi">0</span><span class="o">];</span>
+<span class="n">DataFrame</span> <span class="n">testData</span> <span class="o">=</span> <span class="n">splits</span><span class="o">[</span><span class="mi">1</span><span class="o">];</span>
+
+<span class="c1">// Train a DecisionTree model.</span>
+<span class="n">DecisionTreeRegressor</span> <span class="n">dt</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">DecisionTreeRegressor</span><span class="o">()</span>
+  <span class="o">.</span><span class="na">setFeaturesCol</span><span class="o">(</span><span class="s">&quot;indexedFeatures&quot;</span><span class="o">);</span>
+
+<span class="c1">// Chain indexer and tree in a Pipeline</span>
+<span class="n">Pipeline</span> <span class="n">pipeline</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">Pipeline</span><span class="o">()</span>
+  <span class="o">.</span><span class="na">setStages</span><span class="o">(</span><span class="k">new</span> <span class="n">PipelineStage</span><span class="o">[]</span> <span class="o">{</span><span class="n">featureIndexer</span><span class="o">,</span> <span class="n">dt</span><span class="o">});</span>
+
+<span class="c1">// Train model.  This also runs the indexer.</span>
+<span class="n">PipelineModel</span> <span class="n">model</span> <span class="o">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">trainingData</span><span class="o">);</span>
+
+<span class="c1">// Make predictions.</span>
+<span class="n">DataFrame</span> <span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="na">transform</span><span class="o">(</span><span class="n">testData</span><span class="o">);</span>
+
+<span class="c1">// Select example rows to display.</span>
+<span class="n">predictions</span><span class="o">.</span><span class="na">select</span><span class="o">(</span><span class="s">&quot;label&quot;</span><span class="o">,</span> <span class="s">&quot;features&quot;</span><span class="o">).</span><span class="na">show</span><span class="o">(</span><span class="mi">5</span><span class="o">);</span>
+
+<span class="c1">// Select (prediction, true label) and compute test error</span>
+<span class="n">RegressionEvaluator</span> <span class="n">evaluator</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">RegressionEvaluator</span><span class="o">()</span>
+  <span class="o">.</span><span class="na">setLabelCol</span><span class="o">(</span><span class="s">&quot;label&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="na">setPredictionCol</span><span class="o">(</span><span class="s">&quot;prediction&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="na">setMetricName</span><span class="o">(</span><span class="s">&quot;rmse&quot;</span><span class="o">);</span>
+<span class="kt">double</span> <span class="n">rmse</span> <span class="o">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="na">evaluate</span><span class="o">(</span><span class="n">predictions</span><span class="o">);</span>
+<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">&quot;Root Mean Squared Error (RMSE) on test data = &quot;</span> <span class="o">+</span> <span class="n">rmse</span><span class="o">);</span>
+
+<span class="n">DecisionTreeRegressionModel</span> <span class="n">treeModel</span> <span class="o">=</span>
+  <span class="o">(</span><span class="n">DecisionTreeRegressionModel</span><span class="o">)(</span><span class="n">model</span><span class="o">.</span><span class="na">stages</span><span class="o">()[</span><span class="mi">1</span><span class="o">]);</span>
+<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">&quot;Learned regression tree model:\n&quot;</span> <span class="o">+</span> <span class="n">treeModel</span><span class="o">.</span><span class="na">toDebugString</span><span class="o">());</span></code></pre></div>
+
+  </div>
+
+<div data-lang="python">
+
+    <p>More details on parameters can be found in the <a href="api/python/pyspark.ml.html#pyspark.ml.regression.DecisionTreeRegressor">Python API documentation</a>.</p>
+
+    <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">pyspark.ml</span> <span class="kn">import</span> <span class="n">Pipeline</span>
+<span class="kn">from</span> <span class="nn">pyspark.ml.regression</span> <span class="kn">import</span> <span class="n">DecisionTreeRegressor</span>
+<span class="kn">from</span> <span class="nn">pyspark.ml.feature</span> <span class="kn">import</span> <span class="n">VectorIndexer</span>
+<span class="kn">from</span> <span class="nn">pyspark.ml.evaluation</span> <span class="kn">import</span> <span class="n">RegressionEvaluator</span>
+<span class="kn">from</span> <span class="nn">pyspark.mllib.util</span> <span class="kn">import</span> <span class="n">MLUtils</span>
+
+<span class="c"># Load and parse the data file, converting it to a DataFrame.</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">MLUtils</span><span class="o">.</span><span class="n">loadLibSVMFile</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="s">&quot;data/mllib/sample_libsvm_data.txt&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">toDF</span><span class="p">()</span>
+
+<span class="c"># Automatically identify categorical features, and index them.</span>
+<span class="c"># We specify maxCategories so features with &gt; 4 distinct values are treated as continuous.</span>
+<span class="n">featureIndexer</span> <span class="o">=</span>\
+    <span class="n">VectorIndexer</span><span class="p">(</span><span class="n">inputCol</span><span class="o">=</span><span class="s">&quot;features&quot;</span><span class="p">,</span> <span class="n">outputCol</span><span class="o">=</span><span class="s">&quot;indexedFeatures&quot;</span><span class="p">,</span> <span class="n">maxCategories</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+<span class="c"># Split the data into training and test sets (30% held out for testing)</span>
+<span class="p">(</span><span class="n">trainingData</span><span class="p">,</span> <span class="n">testData</span><span class="p">)</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">randomSplit</span><span class="p">([</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">])</span>
+
+<span class="c"># Train a DecisionTree model.</span>
+<span class="n">dt</span> <span class="o">=</span> <span class="n">DecisionTreeRegressor</span><span class="p">(</span><span class="n">featuresCol</span><span class="o">=</span><span class="s">&quot;indexedFeatures&quot;</span><span class="p">)</span>
+
+<span class="c"># Chain indexer and tree in a Pipeline</span>
+<span class="n">pipeline</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span><span class="n">stages</span><span class="o">=</span><span class="p">[</span><span class="n">featureIndexer</span><span class="p">,</span> <span class="n">dt</span><span class="p">])</span>
+
+<span class="c"># Train model.  This also runs the indexer.</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">trainingData</span><span class="p">)</span>
+
+<span class="c"># Make predictions.</span>
+<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">testData</span><span class="p">)</span>
+
+<span class="c"># Select example rows to display.</span>
+<span class="n">predictions</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s">&quot;prediction&quot;</span><span class="p">,</span> <span class="s">&quot;label&quot;</span><span class="p">,</span> <span class="s">&quot;features&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+
+<span class="c"># Select (prediction, true label) and compute test error</span>
+<span class="n">evaluator</span> <span class="o">=</span> <span class="n">RegressionEvaluator</span><span class="p">(</span>
+    <span class="n">labelCol</span><span class="o">=</span><span class="s">&quot;label&quot;</span><span class="p">,</span> <span class="n">predictionCol</span><span class="o">=</span><span class="s">&quot;prediction&quot;</span><span class="p">,</span> <span class="n">metricName</span><span class="o">=</span><span class="s">&quot;rmse&quot;</span><span class="p">)</span>
+<span class="n">rmse</span> <span class="o">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span>
+<span class="k">print</span> <span class="s">&quot;Root Mean Squared Error (RMSE) on test data = </span><span class="si">%g</span><span class="s">&quot;</span> <span class="o">%</span> <span class="n">rmse</span>
+
+<span class="n">treeModel</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">stages</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+<span class="k">print</span> <span class="n">treeModel</span> <span class="c"># summary only</span></code></pre></div>
+
+  </div>
+
+</div>
+
+
+        </div> <!-- /container -->
+
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author	Reynold Xin <rxin@apache.org>	2015-09-08 23:20:31 +0000
committer	Reynold Xin <rxin@apache.org>	2015-09-08 23:20:31 +0000
commit	443d7fc272a34a818df4dd589bb251ec1087ae11 (patch)
tree	58b176846888d8824cd113146bd59568ea5354f6 /site/docs/1.5.0/ml-decision-tree.html
parent	1037fcd3d980ca1bf8e79ccfecd1f5234545b6ff (diff)
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