Implements Under/Oversampling for Probability Estimation
AdaBoost Classifier
Simulate data from the circle model.
Simulate data from the Friedman model
Function to compute predicted quantiles
Return indices to be used for jittered data in oversampling
Return indices to be used in original data for undersampling
Jittering with Over/Under Sampling
JOUSBoost: A package for probability estimation
Create predictions from AdaBoost fit
Create predictions
Print a summary of adaboost fit.
Print a summary of jous fit.
Implements under/oversampling for probability estimation. To be used with machine learning methods such as AdaBoost, random forests, etc.