26 lines
1.5 KiB
Plaintext
26 lines
1.5 KiB
Plaintext
Solve the warnings issue; right now there is a warning ignore in method/__init__.py:
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Add 'platt' to calib options in EMQ?
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Allow n_prevpoints in APP to be specified by a user-defined grid?
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Add the fix suggested by Alexander?
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"For a more general application, I would maybe first establish a per-class threshold value of plausible prevalence
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based on the number of actual positives and the required sample size; e.g., for sample_size=100 and actual
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positives [10, 100, 500] -> [0.1, 1.0, 1.0], meaning that class 0 can be sampled at most at 0.1 prevalence, while
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the others can be sampled up to 1. prevalence. Then, when a prevalence value is requested, e.g., [0.33, 0.33, 0.33],
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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
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scale each value by per-class thresholds, i.e., [0.33*0.1, 0.33*1, 0.33*1]/sum."
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- This affects LabelledCollection
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- This functionality should be accessible via sampling protocols and evaluation functions
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- [TODO] document confidence in manuals
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- [TODO] Test the return_type="index" in protocols and finish the "distributing_samples.py" example
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- [TODO] Add EDy (an implementation is available at quantificationlib)
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- [TODO] add ensemble methods SC-MQ, MC-SQ, MC-MQ
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- [TODO] add HistNetQ
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- [TODO] add CDE-iteration and Bayes-CDE methods
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- [TODO] add Friedman's method and DeBias
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- [TODO] check ignore warning stuff
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check https://docs.python.org/3/library/warnings.html#temporarily-suppressing-warnings
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- [TODO] nmd and md are not selectable from qp.evaluation.evaluate as a string |