Evaluation compares predictions with relevant outcomes using a metric suited to the task. Report the data split and baseline alongside a score.
Before you begin
Know Python, tabular data handling and basic statistics. Use fictional or permitted data and identify the prediction or evaluation task.
Work through the idea
Keep an experiment record with the dataset, split, baseline, metric and limitations. Choose information that would genuinely be available at the time of prediction. Compare the method with a simple alternative and inspect mistakes. A learning experiment should not be presented as a validated production decision system.
- Match metric to error cost
- Keep evaluation independent
- Inspect subgroup and failure cases
Practice task
Compare two fictional classifiers with different false-positive and false-negative counts.
Check your result
Explain why the preferred model depends on the application rather than a single universal score.
A mistake to avoid
Do not report a metric without its dataset and evaluation procedure.
Keep a review note
Save the inputs or scenario, your reasoning and the result you checked. Explain one assumption and one case that needs more work. Revise the exercise after feedback and distinguish what you demonstrated from what remains untested.
Official reference for further study
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