Overview
Model evaluation is a crucial step in the development of artificial intelligence systems. It helps us understand how well a model performs on given tasks. By assessing the model's accuracy and reliability, we can make informed decisions about its effectiveness. This process involves using various metrics and techniques to ensure that our AI models are not only accurate but also generalize well to new data.
📚 Key Learning Objectives
- ✓ Define model evaluation and its importance in AI.
- ✓ Identify key metrics used for evaluating models.
- ✓ Explain the difference between training and testing data.
- ✓ Apply evaluation techniques to assess model performance.
- ✓ Interpret evaluation results to improve model accuracy.
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