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3 cards from this section. Quick, good the night before a test.
What is the formula for calculating accuracy?
Think about how we assess the prediction results.
Why is accuracy important in model evaluation?
Consider why we need to assess model performance.
What is the formula for accuracy?
Remember the basic formula we discussed.
True or False: Accuracy is the best metric for evaluating all machine learning models.
Think about our discussion on class balance.
Imagine you have a dataset of 1000 images, where 900 are cats and 100 are dogs. If your model predicts every image as a cat, what is the accuracy, and why might this be a poor predictor of performance?
Consider the impact of imbalanced data on your calculation.
Develop a scenario where a machine learning model achieves high accuracy but fails to meet the needs of stakeholders. Describe the metric alongside accuracy that would provide a complete picture in this instance.
Relate back to how we evaluate all aspects of performance.
5 more questions in this section. Enroll free to practise them all.
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