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[SPARK-3227] [mllib] Added migration guide for v1.0 to v1.1 #2146

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[SPARK-3227] [mllib] Added migration guide for v1.0 to v1.1 #2146

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Added migration guide for v1.0 to v1.1. The only updates are in Deci…
jkbradley Aug 26, 2014
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small edit to doc
jkbradley Aug 27, 2014
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28 changes: 27 additions & 1 deletion docs/mllib-guide.md
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Expand Up @@ -54,6 +54,32 @@ To use MLlib in Python, you will need [NumPy](http://www.numpy.org) version 1.4

# Migration Guide

## From 1.0 to 1.1

The only API changes in MLlib v1.1 are in
[`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree),
which continues to be an experimental API in MLlib 1.1:

1. *(Breaking change)* The meaning of tree depth has been changed by 1 in order to match
the implementations of trees in
[scikit-learn](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.tree)
and in [rpart](http://cran.r-project.org/web/packages/rpart/index.html).
In MLlib v1.0, a depth-1 tree had 1 leaf node, and a depth-2 tree had 1 root node and 2 leaf nodes.
In MLlib v1.1, a depth-0 tree has 1 leaf node, and a depth-1 tree has 1 root node and 2 leaf nodes.
This depth is specified by the `maxDepth` parameter in
[`Strategy`](api/scala/index.html#org.apache.spark.mllib.tree.configuration.Strategy)
or via [`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree)
static `trainClassifier` and `trainRegressor` methods.

2. *(Non-breaking change)* We recommend using the newly added `trainClassifier` and `trainRegressor`
methods to build a [`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree),
rather than using the old parameter class `Strategy`. These new training methods explicitly
separate classification and regression, and they replace specialized parameter types with
simple `String` types.

Examples of the new, recommended `trainClassifier` and `trainRegressor` are given in the
[Decision Trees Guide](mllib-decision-tree.html#examples).

## From 0.9 to 1.0

In MLlib v1.0, we support both dense and sparse input in a unified way, which introduces a few
Expand All @@ -79,7 +105,7 @@ val vector: Vector = Vectors.dense(array) // a dense vector

[`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$) provides factory methods to create sparse vectors.

*Note*. Scala imports `scala.collection.immutable.Vector` by default, so you have to import `org.apache.spark.mllib.linalg.Vector` explicitly to use MLlib's `Vector`.
*Note*: Scala imports `scala.collection.immutable.Vector` by default, so you have to import `org.apache.spark.mllib.linalg.Vector` explicitly to use MLlib's `Vector`.

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