graboo_fit1: Function that implement one weak model step of Gradient...
In StevenBoys/Ensemblelearn: Use Ensemble learning to better prediction.

Description Usage Arguments Value Examples

Function that implement one weak model step of Gradient Boosting in regression

1	graboo_fit1(data, fweak = graboo_reg, loss = mse, eta = 0.1)

`data`	- list of data that fweak need
`fweak`	- function that generates estimate from weak model based on input, its default value is graboo_reg
`loss`	- the loss function used, its default value is the mean of the square error
`eta`	- the step size we use to update the total estimate each time, its default value is 0.1

A trained model function based on the one implementation of the weak model, whose input is the independent variables.

data <- list(x = matrix(rnorm(1000), 200, 5))
data$y <- data$x %*% rnorm(5) + rnorm(200, 0, 3)
data$last_est <- rep(0, ncol(data$x))
graboo_fit1(data)