Description Usage Arguments Value See Also
Adjusts the posterior probability of a classifier based on unbalanced datasets. In classification model where the negative data is randomly under-sampled and all the positive data is used, the adjustment factor (beta) is p(s=1|-) = p(+)/p(-). I.e., the probability that a negative datapoint is selected in the classifier. beta ~ N+/N-.
1 | posteriorBalance(probs, beta = NULL, Nplus, Nminus)
|
probs |
The original posterior probability. |
beta |
The adjustment factor. |
Nplus |
The number of positive examples in the real data. Only used if beta is NULL. |
Nminus |
The number of negative examples in the real data. Only used if beta is NULL. |
The adjusted posterior probability.
Dal Pozzolo, Andrea, et al. "Calibrating probability with undersampling for unbalanced classification." Computational Intelligence, 2015 IEEE Symposium Series on. IEEE, 2015.
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