Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/8244#discussion_r37357835
--- Diff: docs/ml-decision-tree.md ---
@@ -0,0 +1,506 @@
+---
+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)
--- End diff --
minor: We could use `CrossValidator` to simplify the example. The pipeline
becomes `[labelIndexer, vectorIndex, cv(dt, evaluator)]`. Just want to separate
pipeline construction from fitting, no strong preference.
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