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This sample demonstrates how a model is generated out of a data set using the decision tree 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.

Building the sample

Execute the following command to download the source code of the product: git clone https://github.com/wso2/product-ml.git

Executing the sample

Once the sample is successfully executed, you can obtain the following output.

1. Follow the steps below if you already executed a sample before.

2. Execute the following command to remove the databases created: rm -rf repository/database/WSO2ML_DB.*
3. Navigate to <ML_HOME>/bin/ directory using the CLI.
Restart the WSO2 ML server by executing the following command: ./wso2server.sh -Dsetup Download and install jq (CLI JSON processor). For instructions, see jq Documentation.
4. 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/rest-apidefault/decision-tree/ directory using the CLI.

   Info<ML_HOME> refers to the downloaded product-ml directory with the source code of the product

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

Output of the sample

 Once Once the sample is successfully executed, you can obtain the following output.view the summary and the prediction of the model as described below.

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You can change the location where the output model file is saved, by defining the folder path of the required location for the value of the location element in the <ML_HOME>/samples/rest-api/decision-tree/create-model-storage file as shown below.

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Viewing the model summary

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

1. Log in to the ML UI from your Web browser using admin/admin credentials and the following URL: https://<ML_HOST>:<ML_PORT>/ml

2. Click the Projects button as shown below.
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3. Click MODELS button of the new analysis which you created by executing the sample as shown below.
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4. Click VIEW of the built new model as shown below.
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   You view the summary of the built model as shown below.
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