rusboost: RUSBoost for two-class problems

In farr: Data and Code for Financial Accounting Research

RUSBoost for two-class problems

Description

RUSBoost for two-class problems

Usage

rusboost(formula, df, size, ir = 1, learn_rate = 1, rus = TRUE, control)

Arguments

formula	A formula specify predictors and target variable. Target variable should be a factor of 0 and 1. Predictors can be either numerical and categorical.
df	A df frame used for training the model, i.e. training set.
size	Ensemble size, i.e. number of weak learners in the ensemble model
ir	Imbalance ratio. Specifies how many times the under-sampled majority instances are over minority instances.
learn_rate	Default of 1.
rus	TRUE for random undersampling; FALSE for AdaBoost with full sample
control	Control object passed onto rpart function.

Value

rusboost object

farr documentation built on Feb. 16, 2023, 8:11 p.m.