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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" id="markdown-toc-overview">Overview</a></li>
<li><a href="#inputs-and-outputs" id="markdown-toc-inputs-and-outputs">Inputs and Outputs</a> <ul>
<li><a href="#input-columns" id="markdown-toc-input-columns">Input Columns</a></li>
<li><a href="#output-columns" id="markdown-toc-output-columns">Output Columns</a></li>
</ul>
</li>
<li><a href="#examples" id="markdown-toc-examples">Examples</a> <ul>
<li><a href="#classification" id="markdown-toc-classification">Classification</a></li>
<li><a href="#regression" id="markdown-toc-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">"data/mllib/sample_libsvm_data.txt"</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">"label"</span><span class="o">)</span>
<span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"indexedLabel"</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">"features"</span><span class="o">)</span>
<span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"indexedFeatures"</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 > 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">"indexedLabel"</span><span class="o">)</span>
<span class="o">.</span><span class="n">setFeaturesCol</span><span class="o">(</span><span class="s">"indexedFeatures"</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">"prediction"</span><span class="o">)</span>
<span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"predictedLabel"</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">"predictedLabel"</span><span class="o">,</span> <span class="s">"label"</span><span class="o">,</span> <span class="s">"features"</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">"indexedLabel"</span><span class="o">)</span>
<span class="o">.</span><span class="n">setPredictionCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span>
<span class="o">.</span><span class="n">setMetricName</span><span class="o">(</span><span class="s">"precision"</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">"Test Error = "</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">"Learned classification tree model:\n"</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"><</span><span class="n">LabeledPoint</span><span class="o">></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">"data/mllib/sample_libsvm_data.txt"</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">"label"</span><span class="o">)</span>
<span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"indexedLabel"</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">"features"</span><span class="o">)</span>
<span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"indexedFeatures"</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 > 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">"indexedLabel"</span><span class="o">)</span>
<span class="o">.</span><span class="na">setFeaturesCol</span><span class="o">(</span><span class="s">"indexedFeatures"</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">"prediction"</span><span class="o">)</span>
<span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"predictedLabel"</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">"predictedLabel"</span><span class="o">,</span> <span class="s">"label"</span><span class="o">,</span> <span class="s">"features"</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">"indexedLabel"</span><span class="o">)</span>
<span class="o">.</span><span class="na">setPredictionCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span>
<span class="o">.</span><span class="na">setMetricName</span><span class="o">(</span><span class="s">"precision"</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">"Test Error = "</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">"Learned classification tree model:\n"</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">"data/mllib/sample_libsvm_data.txt"</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">"label"</span><span class="p">,</span> <span class="n">outputCol</span><span class="o">=</span><span class="s">"indexedLabel"</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 > 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">"features"</span><span class="p">,</span> <span class="n">outputCol</span><span class="o">=</span><span class="s">"indexedFeatures"</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">"indexedLabel"</span><span class="p">,</span> <span class="n">featuresCol</span><span class="o">=</span><span class="s">"indexedFeatures"</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">"prediction"</span><span class="p">,</span> <span class="s">"indexedLabel"</span><span class="p">,</span> <span class="s">"features"</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">"indexedLabel"</span><span class="p">,</span> <span class="n">predictionCol</span><span class="o">=</span><span class="s">"prediction"</span><span class="p">,</span> <span class="n">metricName</span><span class="o">=</span><span class="s">"precision"</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">"Test Error = </span><span class="si">%g</span><span class="s">"</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">"data/mllib/sample_libsvm_data.txt"</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 > 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">"features"</span><span class="o">)</span>
<span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"indexedFeatures"</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">"label"</span><span class="o">)</span>
<span class="o">.</span><span class="n">setFeaturesCol</span><span class="o">(</span><span class="s">"indexedFeatures"</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">"prediction"</span><span class="o">,</span> <span class="s">"label"</span><span class="o">,</span> <span class="s">"features"</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">"label"</span><span class="o">)</span>
<span class="o">.</span><span class="n">setPredictionCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span>
<span class="o">.</span><span class="n">setMetricName</span><span class="o">(</span><span class="s">"rmse"</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">"Root Mean Squared Error (RMSE) on test data = "</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">"Learned regression tree model:\n"</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"><</span><span class="n">LabeledPoint</span><span class="o">></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">"data/mllib/sample_libsvm_data.txt"</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 > 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">"features"</span><span class="o">)</span>
<span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"indexedFeatures"</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">"indexedFeatures"</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">"label"</span><span class="o">,</span> <span class="s">"features"</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">"label"</span><span class="o">)</span>
<span class="o">.</span><span class="na">setPredictionCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span>
<span class="o">.</span><span class="na">setMetricName</span><span class="o">(</span><span class="s">"rmse"</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">"Root Mean Squared Error (RMSE) on test data = "</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">"Learned regression tree model:\n"</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">"data/mllib/sample_libsvm_data.txt"</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 > 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">"features"</span><span class="p">,</span> <span class="n">outputCol</span><span class="o">=</span><span class="s">"indexedFeatures"</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">"indexedFeatures"</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">"prediction"</span><span class="p">,</span> <span class="s">"label"</span><span class="p">,</span> <span class="s">"features"</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">"label"</span><span class="p">,</span> <span class="n">predictionCol</span><span class="o">=</span><span class="s">"prediction"</span><span class="p">,</span> <span class="n">metricName</span><span class="o">=</span><span class="s">"rmse"</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">"Root Mean Squared Error (RMSE) on test data = </span><span class="si">%g</span><span class="s">"</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>
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