Introduction

This quick start page shows how to run the Breiman example. It implements the test procedure described in Breiman's paper 1.
The basic algorithm is as follows :

• repeat I iterations
• foreach iteration do
  • 10% of the dataset is kept apart as a testing set
  • build two forests using the training set, one with m=int(log2(M)+1) (called Random-Input) and one with m=1 (called Single-Input)
  • choose the forest that gave the lowest oob error estimation to compute the test set error
  • compute the test set error using the Single Input Forest (test error), this demonstrates that even with m=1, Decision Forests give comparable results to greater values of m
  • compute the mean test set error using every tree of the chosen forest (tree error). This should indicate how well a single Decision Tree performs
• compute the mean test error for all iterations
• compute the mean tree error for all iterations

Steps

Download the data

• The current implementation is compatible with the UCI repository file format. Here are links to some of the datasets used in Breiman's paper:
  • glass : http://archive.ics.uci.edu/ml/datasets/Glass+Identification
  • breast cancer : http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
  • diabetes : http://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes
  • sonar : http://archive.ics.uci.edu/ml/datasets/Connectionist+Bench+(Sonar,+Mines+vs.+Rocks)
  • ionosphere : http://archive.ics.uci.edu/ml/datasets/Ionosphere
  • vehicle : http://archive.ics.uci.edu/ml/datasets/Statlog+(Vehicle+Silhouettes)
  • german : http://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit+Data)
• Put the data in HDFS:

  $HADOOP_HOME/bin/hadoop fs -put <PATH TO DATA> testdata

Build the Job files

• In $MAHOUT_HOME/ run:

  mvn install -DskipTests

Generate a file descriptor for the dataset:

for the glass dataset (glass.data), run :

$HADOOP_HOME/bin/hadoop jar $MAHOUT_HOME/core/target/mahout-core-<VERSION>.job org.apache.mahout.df.tools.Describe -p testdata/glass.data -f testdata/glass.info -d I 9 N L

The "I 9 N L" string indicates the nature of the variables. which means 1 ignored(I) attribute, followed by 9 numerical(N) attributes, followed by the label(L)

• you can also use C for categorical (nominal) attributes

Run the example

$HADOOP_HOME/hadoop jar $MAHOUT_HOME/examples/target/mahout-examples-<VERSION>.job org.apache.mahout.df.BreimanExample -d testdata/glass.data -ds testdata/glass.info -i 10 -t 100

which builds 100 trees (-t argument) and repeats the test 10 iterations (-i argument)

• The example outputs the following results:
  • Selection error : mean test error for the selected forest on all iterations
  • Single Input error : mean test error for the single input forest on all iterations
  • One Tree error : mean single tree error on all iterations
  • Mean Random Input Time : mean build time for random input forests on all iterations
  • Mean Single Input Time : mean build time for single input forests on all iterations