Zansa › Answers › Forecasting and data science
Can I explain why a machine-learning forecast gave a particular number?
Often yes, to a useful degree. Simple models show their drivers directly, while complex ones can be examined with tools such as feature importance and Shapley-value explanations, which estimate how much each input pushed a prediction up or down. These explain the model's behaviour, though not necessarily real-world cause.
Explanations help managers trust and challenge forecasts, for example by seeing that a high forecast was driven by a planned promotion and a holiday.
Cautions:
- Importance is not causation.
- Correlated inputs can share credit in confusing ways.
- Explanations are approximate summaries.
If full transparency is required, such as for regulated decisions about people, prefer inherently interpretable models. Always pair explanations with accuracy evidence and a sanity check by domain experts.
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