GBM_Model {pomodoro} | R Documentation |
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{\pi_i} = \frac{1}{1 + exp[-f(x)]^\prime}
where f(x)
is in the range of [-\infty,\infty]
and its initial estimate of the model is
f^{(0)}_i=log(\frac{\pi_{i}}{1-\pi_{i}})
,
where \hat{\pi}
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
[Package pomodoro version 3.8.0 Index]