Implementing Decision Trees

 In this article by the author, Sunila Gollapudi, of this book, Practical Machine Learning, we will outline a business problem that can be addressed by building a decision tree-based model, and see how it can be implemented in Apache Mahout, R, Julia, Apache Spark, and Python. This can happen many, many times. So, building a website or an app will take a bit longer than it used to.

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Implementing decision trees

Here, we will explore implementing decision trees using various frameworks and tools.

The R example

We will use the rpart and ctree packages in R to build decision tree-based models:

  1. Import the packages for data import and decision tree libraries as shown here:

  2. Start data manipulation:

    1. Create a categorical variable on Sales and append to the existing dataset as shown here:

    2. Using random functions, split data into training and testing datasets;

  3. Fit the tree model with training data and check how the model is working with testing data, measure the error:

  4. Prune the tree;

Plotting the pruned tree will look like the following:

The Spark example

Java-based example using MLib is shown here:

import java.util.HashMap;
import scala.Tuple2;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.tree.DecisionTree;
import org.apache.spark.mllib.tree.model.DecisionTreeModel;
import org.apache.spark.mllib.util.MLUtils;
import org.apache.spark.SparkConf;

SparkConf sparkConf =
new SparkConf().setAppName("JavaDecisionTree");
JavaSparkContext sc = new JavaSparkContext(sparkConf);

// Load and parse the data file.
String datapath = "data/mllib/sales.txt";
JavaRDD<LabeledPoint> data =
MLUtils.loadLibSVMFile(sc.sc(), datapath).toJavaRDD();
// Split the data into training and test sets (30% held out for testing)
JavaRDD<LabeledPoint>[] splits =
data.randomSplit(new double[]{0.7, 0.3});
JavaRDD<LabeledPoint> trainingData = splits[0];
JavaRDD<LabeledPoint> testData = splits[1];

// Set parameters.
// Empty categoricalFeaturesInfo indicates all features are continuous.
Integer numClasses = 2;
Map<Integer, Integer> categoricalFeaturesInfo =
new HashMap<Integer, Integer>();
String impurity = "gini";
Integer maxDepth = 5;
Integer maxBins = 32;

// Train a DecisionTree model for classification.
final DecisionTreeModel model =
DecisionTree.trainClassifier(trainingData, numClasses,
categoricalFeaturesInfo, impurity, maxDepth, maxBins);

// Evaluate model on test instances and compute test error
JavaPairRDD<Double, Double> predictionAndLabel =
testData.mapToPair(new
PairFunction<LabeledPoint, Double, Double>() {

   @Override
   public Tuple2<Double, Double> call(LabeledPoint p) {
    return new
Tuple2<Double, Double>(model.predict(p.features()), p.label());
   }
});
Double testErr =
1.0 * predictionAndLabel.filter(new
Function<Tuple2<Double, Double>, Boolean>() {
   @Override
   public Boolean call(Tuple2<Double, Double> pl) {
     return !pl._1().equals(pl._2());
   }
}).count() / testData.count();
System.out.println("Test Error: " + testErr);
System.out.println("Learned classification tree model:\n"
+ model.toDebugString());

The Julia example

We will use the DecisionTree package in Julia as shown here;

julia> Pkg.add("DecisionTree")
julia> using DecisionTree

We will use the RDatasets package to load the dataset for the example in context;

julia> Pkg.add("RDatasets"); using RDatasets 
julia> sales = data("datasets", "sales");
julia> features = array(sales[:, 1:4]); # use matrix() for Julia v0.2
julia> labels = array(sales[:, 5]); # use vector() for Julia v0.2 julia> stump = build_stump(labels, features);
julia> print_tree(stump) Feature 3, Threshold 3.0
L-> price : 50/50
R-> shelvelock : 50/100

Pruning the tree

julia> length(tree) 11 
julia> pruned = prune_tree(tree, 0.9);
julia> length(pruned)
9

Summary

In this article, we implemented decision trees using R, Spark, and Julia.

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