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5 cards from this section. Quick, good the night before a test.
What is accuracy in the context of model evaluation?
Think about how we define success for a model.
What does the test set represent?
It's separate from the training data!
What is the primary purpose of evaluating an AI model?
Think about why you would check something after completing it.
True or False: The test set should be used during the training process.
Why would mixing training and testing confuse results?
Given a dataset with 1000 samples where 300 are positive class as actual spam, your model predicts 250 positives. It correctly identifies 225 true positives and marks 25 ham as spam. Calculate accuracy, precision, recall, and F1 score.
Break down each part of the calculation step by step for clarity.
Create your confusion matrix from the predicted data comparing against actual results. Discuss how it identifies areas of improvement in the model.
Visualize your data clearly to see prediction results and think about how this helps improve accuracy.
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