rusboost

Handy functions and data to support a course book for accounting research.
Gow, Ian and Tongqing Ding (2022) 'Accounting Research: An Introductory Course' <https://iangow.github.io/far_book/>.

Ian Gow

farr

Data and Code for Financial Accounting Research

rusboost function

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

Arguments

RUSBoost for two-class problems — rusboost

<dl>

<dt>formula</dt>
<dd>A formula specify predictors and target variable. Target variable should be a factor of 0 and 1. Predictors can be either numerical and categorical.</dd>


<dt>df</dt>
<dd>A df frame used for training the model, i.e. training set.</dd>


<dt>size</dt>
<dd>Ensemble size, i.e. number of weak learners in the ensemble model</dd>


<dt>ir</dt>
<dd>Imbalance ratio. Specifies how many times the under-sampled majority instances are over minority instances.</dd>


<dt>learn_rate</dt>
<dd>Default of 1.</dd>


<dt>rus</dt>
<dd>TRUE for random undersampling; FALSE for AdaBoost with full sample</dd>


<dt>control</dt>
<dd>Control object passed onto rpart function.</dd>

</dl>

rusboost: RUSBoost for two-class problems

Description

Usage

Value

Arguments