RF_Model: Random Forest

In pomodoro: Predictive Power of Linear and Tree Modeling

Random Forest

Description

Random Forest

Usage

RF_Model(Data, xvar, yvar)

Arguments

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

Details

Rather than considering the random sample of m predictors from the total of p predictors in each split, random forest does not consider a majority of the p predictors, and considers in each split a fresh sample of m_{try} which we usually set to m_{try} \approx √{p} Random forests which de-correlate the trees by considering m_{try} \approx √{p} show an improvement over bagged trees m = p.

Value

The output from RF_Model.

Examples

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

pomodoro documentation built on March 26, 2022, 5:05 p.m.