I am developing a marketing (Churn) model that has an event rate of 0.5%. So i
thought to perform oversampling. I mean making the number of events equal to
number of non-events by reducing non-events (50-50 after sampling). After
oversampling, we need to adjust predicted probabilities as it inflates
intercept. I always do it in logistic regression. Does the same adjustment
require for decision tree, random forest or other ensemble techniques? I am
following "Applied Predictive Modeling with R (Caret Package)". The author has
used the same technique (down sampling) but he did not adjust predicted
probability when he score validation and test data. Any help would be highly
appreciated!
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