The models are trained in the cloud service. Due to historical data analysis Creatio determines certain patterns that may further be used for predictions. The data used for model training is not saved in the cloud service. Instead, the cloud service is used to store the prediction patterns. Increasing the volume of historical data increases the accuracy of predictions. Therefore, all models must be retrained regularly.
The training progress bar on the machine learning model page enables you to track the current training stage of a model (Fig. 1).
Once the model is successfully trained, a machine learning model instance is created and activated automatically. Retraining models and saving new instances occurs automatically in the background mode. Retraining frequency is configured in the ML models section.
The list of factors that affect the evaluation metric or the quality of a trained ML model (aka “predictors”) are displayed on the Training tab of the model, at the top of the page (Fig. 2). The numbers show how strongly each factor will affect the prediction result. The factors will be displayed once the model training is complete.
When setting up prediction models, the analysts can use this data to fine-tune the model parameters.