flex: FlexBoost

In mnj: Machine Learning and Judgement

Description

A Flexible Boosting Algorithm With Adaptive Loss Functions

Usage

flex(X, y, n_rounds, interval, width, type, control = rpart.control(cp =
  -1, maxdepth = 1))

Arguments

X	Variable of train data
y	Label of train data
n_rounds	How many trees gonna make
interval	Parameter to change Exp Loss-Function
width	Searching area (more than 1)
type	Tie evaluation option (1 or 2, recommed 2)
control	fix cp = -1, maxdepth = 1 based on AdaBoost

Details

This is a main algorithm of FlexBoost: like other Boosting packages, it returns compatible information. In order to prevent unexpected errors, missing data should not be allowed in input data. Return value is composed of four major parts (e.g. terms, trees, alphas, acc). terms : Input variable information trees : Decision tree information alphas : Weight of weak classifier acc : Train accuracy of each iteration

Value

Returns decision tree informations (e.g. Split criteria, Weight of weak classifier, Train accuracy)

Examples

data <- read.csv(url("http://bit.ly/flex_iris"), TRUE)
flex(data[,1:2], data[,6], 10, 0.1, 3, 2)

mnj documentation built on Oct. 11, 2019, 5:05 p.m.