#Breiman Example

Introduction

This page describes how to run the Breiman example, which implements the test procedure described in Leo Breiman’s paper. The basic algorithm is as follows :

• repeat I iterations
• in each iteration do
• keep 10% of the dataset 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 testset 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

Running the Example

The current implementation is compatible with the UCI repository file format. We’ll show how to run this example on two datasets:

First, we deal with Glass Identification: download the dataset file called glass.data and store it onto your local machine. Next, we must generate the descriptor file glass.info for this dataset with the following command:

bin/mahout org.apache.mahout.classifier.df.tools.Describe -p /path/to/glass.data -f /path/to/glass.info -d I 9 N L


Substitute /path/to/ with the folder where you downloaded the dataset, the argument “I 9 N L” indicates the nature of the variables. Here it means 1 ignored (I) attribute, followed by 9 numerical(N) attributes, followed by the label (L).

Finally, we build and evaluate our random forest classifier as follows:

bin/mahout org.apache.mahout.classifier.df.BreimanExample -d /path/to/glass.data -ds /path/to/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

We can repeat this for a Sonar usecase: download the dataset file called sonar.all-data and store it onto your local machine. Generate the descriptor file sonar.info for this dataset with the following command:

bin/mahout org.apache.mahout.classifier.df.tools.Describe -p /path/to/sonar.all-data -f /path/to/sonar.info -d 60 N L


The argument “60 N L” means 60 numerical(N) attributes, followed by the label (L). Analogous to the previous case, we run the evaluation as follows:

bin/mahout org.apache.mahout.classifier.df.BreimanExample -d /path/to/sonar.all-data -ds /path/to/sonar.info -i 10 -t 100