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authorPatrick Wendell <pwendell@apache.org>2015-06-11 15:32:59 +0000
committerPatrick Wendell <pwendell@apache.org>2015-06-11 15:32:59 +0000
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+<!DOCTYPE html>
+<!--[if lt IE 7]> <html class="no-js lt-ie9 lt-ie8 lt-ie7"> <![endif]-->
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+ <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1">
+ <title>Old Migration Guides - MLlib - Spark 1.4.0 Documentation</title>
+
+ <meta name="description" content="MLlib migration guides from before Spark 1.4.0">
+
+
+
+
+ <link rel="stylesheet" href="css/bootstrap.min.css">
+ <style>
+ body {
+ padding-top: 60px;
+ padding-bottom: 40px;
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+ <div class="brand"><a href="index.html">
+ <img src="img/spark-logo-hd.png" style="height:50px;"/></a><span class="version">1.4.0</span>
+ </div>
+ <ul class="nav">
+ <!--TODO(andyk): Add class="active" attribute to li some how.-->
+ <li><a href="index.html">Overview</a></li>
+
+ <li class="dropdown">
+ <a href="#" class="dropdown-toggle" data-toggle="dropdown">Programming Guides<b class="caret"></b></a>
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+ <li><a href="streaming-programming-guide.html">Spark Streaming</a></li>
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+ <li><a href="mllib-guide.html">MLlib (Machine Learning)</a></li>
+ <li><a href="graphx-programming-guide.html">GraphX (Graph Processing)</a></li>
+ <li><a href="bagel-programming-guide.html">Bagel (Pregel on Spark)</a></li>
+ <li><a href="sparkr.html">SparkR (R on Spark)</a></li>
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+ <!--<p class="navbar-text pull-right"><span class="version-text">v1.4.0</span></p>-->
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+ </div>
+
+ <div class="container" id="content">
+
+ <h1 class="title"><a href="mllib-guide.html">MLlib</a> - Old Migration Guides</h1>
+
+
+ <p>The migration guide for the current Spark version is kept on the <a href="mllib-guide.html#migration-guide">MLlib Programming Guide main page</a>.</p>
+
+<h2 id="from-11-to-12">From 1.1 to 1.2</h2>
+
+<p>The only API changes in MLlib v1.2 are in
+<a href="api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree"><code>DecisionTree</code></a>,
+which continues to be an experimental API in MLlib 1.2:</p>
+
+<ol>
+ <li>
+ <p><em>(Breaking change)</em> The Scala API for classification takes a named argument specifying the number
+of classes. In MLlib v1.1, this argument was called <code>numClasses</code> in Python and
+<code>numClassesForClassification</code> in Scala. In MLlib v1.2, the names are both set to <code>numClasses</code>.
+This <code>numClasses</code> parameter is specified either via
+<a href="api/scala/index.html#org.apache.spark.mllib.tree.configuration.Strategy"><code>Strategy</code></a>
+or via <a href="api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree"><code>DecisionTree</code></a>
+static <code>trainClassifier</code> and <code>trainRegressor</code> methods.</p>
+ </li>
+ <li>
+ <p><em>(Breaking change)</em> The API for
+<a href="api/scala/index.html#org.apache.spark.mllib.tree.model.Node"><code>Node</code></a> has changed.
+This should generally not affect user code, unless the user manually constructs decision trees
+(instead of using the <code>trainClassifier</code> or <code>trainRegressor</code> methods).
+The tree <code>Node</code> now includes more information, including the probability of the predicted label
+(for classification).</p>
+ </li>
+ <li>
+ <p>Printing methods&#8217; output has changed. The <code>toString</code> (Scala/Java) and <code>__repr__</code> (Python) methods used to print the full model; they now print a summary. For the full model, use <code>toDebugString</code>.</p>
+ </li>
+</ol>
+
+<p>Examples in the Spark distribution and examples in the
+<a href="mllib-decision-tree.html#examples">Decision Trees Guide</a> have been updated accordingly.</p>
+
+<h2 id="from-10-to-11">From 1.0 to 1.1</h2>
+
+<p>The only API changes in MLlib v1.1 are in
+<a href="api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree"><code>DecisionTree</code></a>,
+which continues to be an experimental API in MLlib 1.1:</p>
+
+<ol>
+ <li>
+ <p><em>(Breaking change)</em> The meaning of tree depth has been changed by 1 in order to match
+the implementations of trees in
+<a href="http://scikit-learn.org/stable/modules/classes.html#module-sklearn.tree">scikit-learn</a>
+and in <a href="http://cran.r-project.org/web/packages/rpart/index.html">rpart</a>.
+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 <code>maxDepth</code> parameter in
+<a href="api/scala/index.html#org.apache.spark.mllib.tree.configuration.Strategy"><code>Strategy</code></a>
+or via <a href="api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree"><code>DecisionTree</code></a>
+static <code>trainClassifier</code> and <code>trainRegressor</code> methods.</p>
+ </li>
+ <li>
+ <p><em>(Non-breaking change)</em> We recommend using the newly added <code>trainClassifier</code> and <code>trainRegressor</code>
+methods to build a <a href="api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree"><code>DecisionTree</code></a>,
+rather than using the old parameter class <code>Strategy</code>. These new training methods explicitly
+separate classification and regression, and they replace specialized parameter types with
+simple <code>String</code> types.</p>
+ </li>
+</ol>
+
+<p>Examples of the new, recommended <code>trainClassifier</code> and <code>trainRegressor</code> are given in the
+<a href="mllib-decision-tree.html#examples">Decision Trees Guide</a>.</p>
+
+<h2 id="from-09-to-10">From 0.9 to 1.0</h2>
+
+<p>In MLlib v1.0, we support both dense and sparse input in a unified way, which introduces a few
+breaking changes. If your data is sparse, please store it in a sparse format instead of dense to
+take advantage of sparsity in both storage and computation. Details are described below.</p>
+
+
+
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