From the course: Programming Foundations: Artificial Intelligence
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Evaluate model performance
From the course: Programming Foundations: Artificial Intelligence
Evaluate model performance
- Imagine, you're a judge at the model evaluation Olympics. In this Olympics, AI models compete to show off their skills. Just like athletes in different sports, our AI models have their own ways of shining, but how do you decide who takes home the gold? Today, you'll dive into the exciting world of model evaluation metrics, a scoring system for AI performance. Get ready to raise your scorecards and discover what makes an AI model a true winner. Let's first look at common metrics used to evaluate the performance of AI models, starting with accuracy. Accuracy is the ratio of correctly predicted instances to the total instances. Imagine, you're a basketball player shooting hoops. Accuracy is like calculating the percentage of successful shots you make out of the total shots you take. If you shoot 10 times and score eight baskets, your accuracy is 80%. It's a straightforward measure of how often you hit the target. The formula for accuracy is true positives plus true negatives divided by…
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