Pick an algorithm for training the model. Consider trying out all the algorithms for your models and save at least two that are more precise.To illustrate a binary category prediction, the predicting telecom customer churn example from the Machine Learning Toolkit showcase under the predicting fields section is shown. Train your model by using the guided workflow for experiments in the Machine Learning Toolkit.Examples of added fields could include a person’s address, a DNS name, or details about an order. Lookups enrich the search from external sources such as CSV files, databases, output of an API or Splunk KVStore. Consider using lookups to annotate your events with relevant fields for your use case, as not all your fields will be in the events.You only extract fields from the data relevant to the use case, and do this after the data is indexed. If you know how to use regular expressions, the extraction becomes even easier. If the data is unstructured in any arbitrary format, you can use the Splunk Web Interface to extract fields from the GUI. If the data is structured, the Splunk platform can easily extract it by itself. Extract relevant fields for your use case, especially for the real world data that will be used to predict the category value of a field.You will ingest events into the Splunk platform to apply your trained models against them. Ingest events for your use case into the Splunk platform just like any other events, or use a CSV file containing fields which represent real-world data combinations to create your training data. You could prepare or create a CSV file of relevant fields from the ingested data (use the fields command along with the Export button) to train your Machine Learning Toolkit model.Download and install the free app, Splunk Machine Learning Toolkit and its associated app, the OS platform-dependent Python for Scientific Computing for Windows, Linux or Mac from Splunkbase. A general process to predict the binary value of any categorical field
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