Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/8244#discussion_r37358090
--- Diff: docs/ml-decision-tree.md ---
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+---
+layout: global
+title: Decision Trees - SparkML
+displayTitle: <a href="ml-guide.html">ML</a> - Decision Trees
+---
+
+**Table of Contents**
+
+* This will become a table of contents (this text will be scraped).
+{:toc}
+
+
+# Overview
+
+[Decision trees](http://en.wikipedia.org/wiki/Decision_tree_learning)
+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.
+
+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.
+
+Users can find more information about the decision tree algorithm in the
[MLlib Decision Tree guide](mllib-decision-tree.html). In this section, we
demonstrate the Pipelines API for Decision Trees.
+
+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).
+
+Ensembles of trees (Random Forests and Gradient-Boosted Trees) are
described in the [Ensembles guide](ml-ensembles.html).
+
+# Inputs and Outputs (Predictions)
+
+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.
+
+## Input Columns
+
+<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>
+
+## Output Columns
+
+<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>
+
+# Examples
+
+The below examples demonstrate the Pipelines API for Decision Trees. The
main differences between this API and the [original MLlib Decision Tree
API](mllib-decision-tree.html) are:
+
+* support for ML Pipelines
+* separation of Decision Trees for classification vs. regression
+* use of DataFrame metadata to distinguish continuous and categorical
features
+
+
+## Classification
+
+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
`DataFrame` which the Decision Tree algorithm can recognize.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+More details on parameters can be found in the [Scala API
documentation](api/scala/index.html#org.apache.spark.ml.classification.DecisionTreeClassifier).
+
+{% highlight scala %}
+import org.apache.spark.ml.Pipeline
+import org.apache.spark.ml.classification.DecisionTreeClassifier
+import org.apache.spark.ml.classification.DecisionTreeClassificationModel
+import org.apache.spark.ml.feature.{StringIndexer, IndexToString,
VectorIndexer}
+import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
+import org.apache.spark.mllib.util.MLUtils
+
+// Load and parse the data file, converting it to a DataFrame.
+val data = MLUtils.loadLibSVMFile(sc,
"data/mllib/sample_libsvm_data.txt").toDF()
+
+// Index labels, adding metadata to the label column.
+// Fit on whole dataset to include all labels in index.
+val labelIndexer = new StringIndexer()
+ .setInputCol("label")
+ .setOutputCol("indexedLabel")
+ .fit(data)
+// Automatically identify categorical features, and index them.
+val featureIndexer = new VectorIndexer()
+ .setInputCol("features")
+ .setOutputCol("indexedFeatures")
+ .setMaxCategories(4) // features with > 4 distinct values are treated as
continuous
+ .fit(data)
+
+// Split the data into training and test sets (30% held out for testing)
+val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
+
+// Train a DecisionTree model.
+val dt = new DecisionTreeClassifier()
+ .setLabelCol("indexedLabel")
+ .setFeaturesCol("indexedFeatures") // "features" is the default
+
+// Convert indexed labels back to original labels.
+val labelConverter = new IndexToString()
+ .setInputCol("prediction")
+ .setOutputCol("predictedLabel")
+ .setLabels(labelIndexer.labels)
+
+// Chain indexers and tree in a Pipeline
+val pipeline = new Pipeline()
+ .setStages(Array(labelIndexer, featureIndexer, dt, labelConverter))
+
+// Train model. This also runs the indexers.
+val model = pipeline.fit(trainingData)
+
+// Make predictions.
+val predictions = model.transform(testData)
+
+// Select example rows to display.
+predictions.select("predictedLabel", "label", "features").show(5)
+
+// Select (prediction, true label) and compute test error
+val evaluator = new MulticlassClassificationEvaluator()
+ .setLabelCol("indexedLabel")
+ .setPredictionCol("prediction")
+ .setMetricName("precision")
+val accuracy = evaluator.evaluate(predictions)
+println("Test Error = " + (1.0 - accuracy))
+
+val treeModel =
model.stages(2).asInstanceOf[DecisionTreeClassificationModel]
+println("Learned classification tree model:\n" + treeModel.toDebugString)
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+More details on parameters can be found in the [Java API
documentation](api/java/org/apache/spark/ml/classification/DecisionTreeClassifier.html).
+
+{% highlight java %}
+import org.apache.spark.ml.Pipeline;
+import org.apache.spark.ml.PipelineModel;
+import org.apache.spark.ml.PipelineStage;
+import org.apache.spark.ml.classification.DecisionTreeClassifier;
+import org.apache.spark.ml.classification.DecisionTreeClassificationModel;
+import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
+import org.apache.spark.ml.feature.*;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.rdd.RDD;
+import org.apache.spark.sql.DataFrame;
+
+// Load and parse the data file, converting it to a DataFrame.
+RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(),
"data/mllib/sample_libsvm_data.txt");
+DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class);
+
+// Index labels, adding metadata to the label column.
+// Fit on whole dataset to include all labels in index.
+StringIndexerModel labelIndexer = new StringIndexer()
+ .setInputCol("label")
+ .setOutputCol("indexedLabel")
+ .fit(data);
+// Automatically identify categorical features, and index them.
+VectorIndexerModel featureIndexer = new VectorIndexer()
+ .setInputCol("features")
+ .setOutputCol("indexedFeatures")
+ .setMaxCategories(4) // features with > 4 distinct values are treated as
continuous
+ .fit(data);
+
+// Split the data into training and test sets (30% held out for testing)
+DataFrame[] splits = data.randomSplit(new double[]{0.7, 0.3});
--- End diff --
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