Use Parallel Processing for Regression TreeBagger Workflow

This example shows you how to:

• Use an ensemble of bagged regression trees to estimate feature importance.

• Improve computation speed by using parallel computing.

The sample data is a database of 1985 car imports with 205 observations, 25 predictors, and 1 response, which is insurance risk rating, or "symboling." The first 15 variables are numeric and the last 10 are categorical. The symboling index takes integer values from -3 to 3.

Load the sample data and separate it into predictor and response arrays.

load imports-85;
Y = X(:,1);
X = X(:,2:end);

Set up the parallel environment to use the default number of workers. The computer that created this example has six cores.

mypool = parpool

Starting parallel pool (parpool) using the 'local' profile ...
Connected to the parallel pool (number of workers: 6).

mypool = 

 ProcessPool with properties: 

            Connected: true
           NumWorkers: 6
              Cluster: local
        AttachedFiles: {}
    AutoAddClientPath: true
          IdleTimeout: 30 minutes (30 minutes remaining)
          SpmdEnabled: true

Set the options to use parallel processing.

paroptions = statset('UseParallel',true);

Estimate feature importance using leaf size 1 and 5000 trees in parallel. Time the function for comparison purposes.

tic
b = TreeBagger(5000,X,Y,'Method','r','OOBVarImp','on', ...
    'cat',16:25,'MinLeafSize',1,'Options',paroptions);
toc

Elapsed time is 9.873065 seconds.

Perform the same computation in serial for timing comparison.

tic
b = TreeBagger(5000,X,Y,'Method','r','OOBVarImp','on', ...
    'cat',16:25,'MinLeafSize',1);
toc

Elapsed time is 28.092654 seconds.

The results show that computing in parallel takes a fraction of the time it takes to compute serially. Note that the elapsed time can vary depending on your operating system.