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2 cards from this section. Quick, good the night before a test.
What is the formula for calculating accuracy?
Remember the involvement of correct predictions.
Why is accuracy important in AI evaluation?
Think about how well the model can predict outcomes.
What does accuracy measure in AI evaluation?
Think about what determines a model's effectiveness.
True or False: Accuracy is the only important metric to evaluate an AI model.
Consider the limitations of focusing solely on one metric.
An AI model is trained on a dataset of 500 images. After testing, it identifies 390 images correctly. Compare its performance against another model that identifies 350 images correctly. Calculate both models' accuracy and discuss which model performs better and why.
Analyze the need for additional metrics depending on context.
Imagine you are evaluating two classifiers: Classifier A with 92% accuracy but an imbalanced dataset, and Classifier B with 85% accuracy and balanced data. Which classifier would you choose and why?
Consider how performance is affected by data distribution.
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