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This sample demonstrates how a model is generated out of a data set using the linear 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.
Building the sample
- 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.
- Restart the WSO2 ML server by executing the following command: ./
- Execute the following command to execute the sample: sh ./
<ML_HOME>/samples/rest-apidefault/linear-regression/ directory using the CLI.
<ML_HOME> refers to the downloaded
product-ml directory with the source code of the product.
Output of the sample
Once the sample is successfully executed, you can obtain the following output.
view the summary and the prediction of the model as described below.
By default , the sample generates the model in the
directory of your machine. For example, the generated file is in the following format denoting the date and time when it was generated:
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
Viewing the model summary
You can view the summary of the built model using the ML UI as follows.
Log in to the ML UI from your Web browser using
admin/admincredentials and the following URL: https://<ML_HOST>:<ML_PORT>/ml
Click the Projects button as shown below.
- Click MODELS button of the new analysis which you created by executing the sample as shown below.
- Click VIEW of the built new model as shown below.
You view the summary of the built model as shown below.
Viewing the model prediction
The sample executes the generated model on
default/linear-regression/prediction-test data set, and it prints the value
as the prediction result In the CLI logs.