Subkey |
Description |
---|---|
Use |
Percentage of times Object (e.g. patient) resampling is used. |
Method |
One of the methods adopted, see also imbalanced learn <https://imbalanced-learn.readthedocs.io/en/stable/api/>`_. |
sampling_strategy |
Sampling strategy, see also imbalanced learn <https://imbalanced-learn.readthedocs.io/en/stable/api/>`_. |
n_neighbors |
Number of n_neighbors used in resampling. This should be (much) smaller than the number of objects/patients you supply. We sample on a uniform scale: the parameters specify the range (loc, loc + scale). |
k_neighbors |
Number of n_neighbors used in resampling. This should be (much) smaller than the number of objects/patients you supply. We sample on a uniform scale: the parameters specify the range (loc, loc + scale). |
threshold_cleaning |
Threshold for cleaning of samples. We sample on a uniform scale: the parameters specify the range (loc, loc + scale). |