What is the purpose of model evaluation in AI?

The purpose of model evaluation in AI is critically aligned with determining the effectiveness of a model in making predictions. This process involves assessing how well a trained model performs on unseen data, which provides insights into its accuracy, reliability, and overall performance in various scenarios.

By evaluating a model, practitioners can understand how well it generalizes beyond the training data, ensuring that it can make accurate predictions on new, real-world data. This is essential because a model that performs well on training data but poorly on validation or test datasets may lead to poor decision-making when deployed in practical applications.

Evaluation metrics such as accuracy, precision, recall, F1-score, and others help quantify the model's performance, allowing practitioners to compare different models, choose the best one, and make necessary adjustments or improvements. Ultimately, model evaluation is a crucial step in the machine learning workflow that informs users about the quality and readiness of a model for deployment.