Evaluating AI Models
How to evaluate and compare AI models: the criteria, trade-offs, and questions to ask.
Questions in this deck
When comparing two AI models for the same task, what is the most important first step?
Why might you choose a simpler model over a more accurate one in production?
What is the key advantage of testing models on multiple diverse datasets rather than one large dataset?
In A/B testing two recommendation models, Model A increases user clicks by 15% while Model B increases time spent by 25%. How should you decide?
A model achieves 95% accuracy on training data but only 70% on new data. This indicates:
When evaluating models for fairness, what should you examine beyond overall accuracy?
When comparing model performance, why is it important to use the same evaluation dataset for all models?
A medical AI model correctly identifies 90% of diseases but also flags 30% of healthy patients as sick. The main concern is:
What does it mean when we say a model has good 'precision' but poor 'recall'?