GBM_Model: Gradient Boosting Model

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

Gradient Boosting Model

Description

Gradient Boosting Model

Usage

GBM_Model(Data, xvar, yvar)

Arguments

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

Details

Unlike bagging trees, boosting does not use bootstrap sampling, rather each tree is fit using information from previous trees. An event probability of stochastic gradient boosting model is given by

\hat{π_i} = \frac{1}{1 + exp[-f(x)]^\prime}

where f(x) is in the range of [-∞,∞] and its initial estimate of the model is f^{(0)}_i=log(\frac{π_{i}}{1-π_{i}}), where \hat{π} is the estimated sample proportion of a single class from the training set.

Value

The output from GBM_Model.

Examples

yvar <- c("Loan.Type")
sample_data <- sample_data[c(1:120),]
xvar <- c("sex", "married", "age", "havejob", "educ", "political.afl",
"rural", "region", "fin.intermdiaries", "fin.knowldge", "income")
BchMk.GBM <- GBM_Model(sample_data, c(xvar, "networth"), yvar )
BchMk.GBM$finalModel
BchMk.GBM$Roc$auc

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