What is model evaluation in the context of neural networks? Discuss different evaluation metrics used to assess the performance of neural network models.

Model evaluation in the context of neural networks refers to the process of assessing the performance and effectiveness of a trained model on unseen data. It involves measuring how well the model generalizes to new examples and how accurately it can make predictions. Evaluating a neural network model helps determine its reliability, robustness, and suitability for the intended task. Various evaluation metrics are used to quantitatively measure the model's performance. Let's discuss some commonly used evaluation metrics: 1. Accuracy: Accuracy is the most straightforward evaluation metric and measures the proportion of correctly classified examples. It calculates the ratio of correctly predicted samples to the total number of samples. While accuracy provides an overall view of the model's performance, it may not be sufficient for imbalanced datasets where the class distribution is skewed. 2. Precision and Recall: Precision and recall are evaluation metrics commonly used in binary classification tasks. Precision measures the proportion of correctly predicted positiv....