Explainability details are exportable from all Forecaster algorithms except for LightGBM.
Explainability results derive from independent features within algorithms. This data helps to generate richer forecasts. Common feature examples are:
- Marketing campaigns and promotions
- Inventory levels
- Product revenue
Explainability details also pinpoint the impact of the forecast inputs (such as related data), on prediction results. Below is an example of an electric bike company:
- You can load a Forecaster data collection with information on historical oil prices, e-bike prices, and promotion data.
- You can show whether increased oil prices or promotions have a greater sales impact.
Not all features impact prediction results in the same way. Explainability results provide an idea about the degree of influence a single feature has on a prediction result.
The impact of features on forecast results
See below to review the impact of features on forecast results.
