A Testing Dataset is a set of data that is used to evaluate the performance of an AI model. It is used to determine how well the model is able to make predictions or decisions based on the data it has been trained on. It is usually a smaller, representative data sample in comparison to the training dataset used to simulate real-world situations to test the model’s accuracy and reliability. The purpose of the Testing Dataset is to provide a realistic evaluation of the AI model’s performance and to help identify any weaknesses or areas for improvement. It helps ensure that the AI model is reliable and effective before it is deployed in real-world applications.
