Chapter 22 - Suggestion: include an example of the Uncertainty case

[biancazadrozny]

The chapter says:

The sits package provides sits_combine_predictions() to estimate ensemble predictions using probability cubes produced by sits_classify() and optionally post-processed with sits_smooth(). There are two ways to make ensemble predictions from multiple models:

Averaging: In this approach, the predictions of each model are averaged to produce the final prediction. This method works well when the models have similar accuracy and errors.

Uncertainty: Predictions from different models are compared in terms of their uncertainties on a pixel-by-pixel basis; predictions with lower uncertainty are chosen as being more likely to be valid.

However, there are only examples for the "Averaging" case, so it would be difficult for a user to know how to implement the uncertainty case, i.e., if there are any parameters to be set (like the "weights" parameter for Averaging).