How do you test an AI model effectively?

How do you test an AI model effectively?

Testing an AI model involves several steps, including data validation, performance metrics, and real-world scenario testing.

What metrics should I use to evaluate its performance?

Common metrics include accuracy, precision, recall, F1 score, and AUC-ROC, depending on the model type.

Can you explain the difference between precision and recall?

Precision measures the accuracy of positive predictions, while recall measures the ability to find all relevant instances.

How can I improve my model's accuracy?

You can improve accuracy by optimizing hyperparameters, using more data, or employing techniques like cross-validation.