deepboost (version 0.1.5)

deepboost.default: Main function for deepboost model creation

Description

Main function for deepboost model creation

Usage

deepboost.default(x, y, instance_weights = NULL, tree_depth = 5, num_iter = 1, beta = 0, lambda = 0.05, loss_type = "l", verbose = TRUE)

Arguments

x

A data.frame of samples' values

y

A data.frame of samples's labels

instance_weights

The weight of each example

tree_depth

maximum depth for a single decision tree in the model

num_iter

number of iterations = number of trees in ensemble

beta

regularisation for scores (L1)

lambda

regularisation for tree depth

loss_type

- "l" logistic, "e" exponential

verbose

- print extra data while training TRUE / FALSE

Value

A trained Deepbost model

Examples

deepboost.default(data.frame(x1=rep(c(0,0,1,1),2),x2=rep(c(0,1,0,1),2)),
 factor(rep(c(0,0,0,1),2)),num_iter=1)
deepboost.default(data.frame(x1=rep(c(0,0,1,1),2),x2=rep(c(0,1,0,1),2)),
 factor(rep(c(0,0,0,1),2)),
 num_iter=2, beta=0.1, lambda=0.00125)