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.
Follow the steps below to set up the prerequisites before you start.
- Download WSO2 Machine Learner, and start the server. For information on setting up and running WSO2 ML, see Getting Started.
- Download and install jq (CLI JSON processor). For instructions, see jq Documentation.
- 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.
<ML_HOME>/samples/default/random-forest-regression/directory using the CLI.
- Execute the following command to execute the sample: ./
Analyzing the output
You can view the summary of the built model using the ML UI as follows.
Open the ML UI using the
https://<ML_HOST>:<ML_PORT>/mlURL. Enter admin as both the user name and the password to log in.
Click the Projects button as shown below.
- Click Models to view the models of the
wso2-ml-random-forest-regression-sample-analysisanalysis that was created when the sample was executed.
- 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.