Add 'platt' to calib options in EMQ?

Allow n_prevpoints in APP to be specified by a user-defined grid?

Add the fix suggested by Alexander:

For a more general application, I would maybe first establish a per-class threshold value of plausible prevalence
based on the number of actual positives and the required sample size; e.g., for sample_size=100 and actual
positives [10, 100, 500] -> [0.1, 1.0, 1.0], meaning that class 0 can be sampled at most at 0.1 prevalence, while
the others can be sampled up to 1. prevalence. Then, when a prevalence value is requested, e.g., [0.33, 0.33, 0.33],
we may either clip each value and normalize (as you suggest for the extreme case, e.g., [0.1, 0.33, 0.33]/sum) or
scale each value by per-class thresholds, i.e., [0.33*0.1, 0.33*1, 0.33*1]/sum.
- This affects LabelledCollection
- This functionality should be accessible via sampling protocols and evaluation functions

- [TODO] Test the return_type="index" in protocols and finish the "distributing_samples.py" example
- [TODO] add ensemble methods SC-MQ, MC-SQ, MC-MQ
- [TODO] add CDE-iteration and Bayes-CDE methods
- [TODO] add Friedman's method and DeBias
- [TODO] check ignore warning stuff
    check https://docs.python.org/3/library/warnings.html#temporarily-suppressing-warnings
- [TODO] nmd and md are not selectable from qp.evaluation.evaluate as a string
- [TODO] add https://www.kaggle.com/datasets/Cornell-University/arxiv dataset? in the paper "Online Adaptation to
    Label Distribution Shift" they say it provides a natural label shift over time
- [TODO] add Bhattacharyya distance (https://en.wikipedia.org/wiki/Bhattacharyya_distance)
    (which is actually not a proper distance)