An explanation describes aspects of a model's behavior under specific assumptions. Feature importance or a local explanation is not proof of causation or complete model reliability.
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.
- Choose the audience and question
- Distinguish global and local explanations
- Check explanation stability
Practice task
Explain one fictional prediction and compare it with a nearby example.
Check your result
State which inputs influence the model and what the explanation cannot establish.
A mistake to avoid
Do not present an explanation graphic as evidence that a model is unbiased or correct.
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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