How can we assess the quality of an AI model?

How can we assess the quality of an AI model?

To assess the quality of an AI model, we typically look at metrics like accuracy, precision, recall, and F1 score.

What is the difference between precision and recall?

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

Are there any specific tools for evaluating AI models?

Yes, tools like TensorBoard, MLflow, and Scikit-learn provide functionalities for model evaluation and visualization.

How important is dataset quality in model evaluation?

Dataset quality is crucial; a model trained on poor-quality data will likely perform poorly, regardless of its architecture.