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Configure a Machine Learning Script That has Initial Training Data

These steps describe how to configure a machine learning script for which an initial set of training data already exists.

Note: A script already contains an initial set of training data if you created it by copying a script that was delivered as part of the GE Digital APM baseline Catalog content. If you instead created a completely new script, you should instead follow the steps to configure and initially train a new machine learning script.

Before You Begin

• Create a machine learning script by copying an existing script that contains initial training data.

• Create a query or dataset of data that you want to use to test (and eventually incrementally train) the script. See the table below for details.

Steps

1. Access the script that you want to configure.

2. Configure the script as described in the following table.

   Section	Description	Required / Optional
   Standard List	Select the standard list that is relevant to the script that you are testing.	Required
   Query or Dataset	Select to browse the Catalog and select the query or dataset that you want to use to test (and incrementally train) the script. The query or dataset that you select must meet the following requirements:  Contains data that is relevant to the script. Contains data for which you want to predict a certain value. See About Prediction Improvement for details. Notes: Fields that are used in a query but hidden from display will be ignored by the script. Hyperlinks defined for columns in a query will appear on the Test Results tab. This feature allows you to easily access associated records while testing a script. For example, a column containing work order numbers could be configured with a link to the full work order. Ensure that the query you use is a Select query. Other types of queries will cause errors to occur. Tip: For example, to test the IsAFailure.py script, the query should include the short and long descriptions of the work history event, the work order priority, and the breakdown indicator.	Required
   Field to Classify	Select a column from the query or dataset that contains the current values that the script is attempting to predict. Tip: For example, when testing the IsAFailure.py script, select the breakdown indicator column as the result column.	Required
   Standard List Reference Field	Select a column from the query or dataset that contains values that identify sub-sets of the specified Standard List (e.g., Equipment Class values). When the script is run, values in the specified field are compared to values in the List Reference field in Classifier Standard List records to determine which sub-set(s) of the script’s Standard List to use.	Optional
   Classifier Input Fields	Select the check box for each column in the query that you want to use as an input to the script. Tip: By default, the field selected in the Standard List Reference Field box is not included as an input to the script. If you want the script to process values in this field, include the column twice in your query or dataset.	Required

   The script is configured.

   

3. Select .

What's Next? 

• Test the script

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