This documentation is for Machine Learner 1.1.0. View documentation for the latest release.
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Introduction

This sample demonstrates how a model is generated out of a data set using the Random Forest Regression algorithm. The sample uses a data set to generate a model, which is divided into two sets for training and testing.

Prerequisites

Follow the steps below to set up the prerequisites before you start.

  1. Download WSO2 Machine Learner, and start the server. For information on setting up and running WSO2 ML, see Getting Started.
  2. Download and install jq (CLI JSON processor). For instructions, see jq Documentation.
  3. If you are using Mac OS X, download and install GNU stream editor (sed). For instructions, see GNU sed Documentation.

Executing the sample

Follow the steps below to execute the sample.

  1. Navigate to <ML_HOME>/samples/default/random-forest-regression/ directory using the CLI.

  2. Execute the following command to execute the sample: ./model-generation.sh

Analyzing the output

You can view the summary of the built model using the ML UI as follows.

  1. Open the ML UI using the https://<ML_HOST>:<ML_PORT>/ml URL. Enter admin as both the user name and the password to log in.

  2. Click the Projects button as shown below.
    click Projects button

  3. Click Models to view the models of the wso2-ml-random-forest-regression-sample-analysis analysis that was created when the sample was executed.
  4. Click View on the model as shown below.

    The summary of the model is displayed as follows. 

Viewing the model prediction

The sample executes the generated model on the <ML_HOME>/samples/default/random-forest-regression/prediction-test data set, and it prints the value [10.459078537423672] as the prediction result in the CLI logs.

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