Validation of a forecast model involves training the model with a portion of data (referred to as your training data) and then testing the model with a different portion (referred to as your test data).
For forecasting tasks, the training data is a prefix of the data and the test data is a suffix of the data that is withheld to compare against the forecasts.
Validating a trained model with the test set can be performed several ways, depending on the type of model. Each assistant provides methods in the Validate
section, which is displayed after you train a model.
A model is ready to be deployed after you have validated it and are comfortable with its performance. Deployment actions are usually categorized as:
- Generate a forecast to use directly or as input to other analytics applications
- Detect outliners and anomalies to help improve the overall process
- Trigger an action or alert of a needed decision
The Splunk Machine Learning Toolkit makes deploying and sharing the results...