Читать книгу Machine Learning with Dynamics 365 and Power Platform - Vinnie Bansal - Страница 32

Once the machine learning model has been properly trained on a given dataset, then we have to test the model. In this step, we check for the accuracy of the model by rendering a test dataset to it. Testing the model is important to find out the percentage accuracy of the model as per the project requirement or given problem.

The input of this validation stage is the trained model produced by the earlier step in the model learning stage, and the output is a validated model that provides enough information to allow users to check whether the machine learning model is appropriate for its intended purpose. Thus, this validation stage of the machine learning lifecycle deals with whether the model is working properly as desired or not when fed with unseen inputs. Thus, model validation is the process that evaluates a trained model on a test dataset. This step renders the generalization ability of the trained model.