BAG_Model: Bagging Model

In seymakalay/pomodoro: Predictive Power of Linear and Tree Modeling

Bagging Model

Description

Bagging Model

Usage

BAG_Model(Data, xvar, yvar)

Arguments

Data	The name of the Dataset.
xvar	X variables.
yvar	Y variable.

Details

Decision trees suffer from high variance (If we split the training data-set randomly into two parts and set a decision tree to both parts, the results might be quite different). Bagging is an ensemble procedure which reduces the variance and increases the prediction accuracy of a statistical learning method by considering many training sets (\hat{f}^{1}(x),\hat{f}^{2}(x),…,\hat{f}^{B}(x)) from the population. Since we can not have multiple training-sets, from a single training data-set, we can generate B different bootstrapped training data-sets (\hat{f}^{*1}(x), \hat{f}^{*2}(x), …,\hat{f}^{*B}(x)) by each B trees and take a majority vote. Therefore, bagging for classification problem defined as

\hat{f}(x)=arg\max_{k}\hat{f}^{*b}(x)

Value

The output from BAG_Model.

Examples

yvar <- c("Loan.Type")
sample_data <- sample_data[c(1:750),]
xvar <- c("sex", "married", "age", "havejob", "educ", "political.afl",
"rural", "region", "fin.intermdiaries", "fin.knowldge", "income")
BchMk.BAG <- BAG_Model(sample_data, c(xvar, "networth"), yvar )
BchMk.BAG$Roc$auc

seymakalay/pomodoro documentation built on May 23, 2022, 3:25 p.m.