Adabag: Bagging classifier

In rishi1226/classrish: Functions for reproducing analysis of classification

Description

Classify the input with a Bagging classifier. Function uses a vector of number of iterations to search for the best training error. Once the best parameters have been identified, it uses them to predict on the test data.

Usage

Adabag(data, mfinal.seq)

Arguments

data	A list generated by the function PrepareData. It is a list with two components. One is a vector of labels, the other is a data frame of features. The other element of the list is a data frame of test features.
mfinal.seq	A vector of values of number of iterations that should be tested.

Value

A list with a dataframe of errors a vector of predictions with the best parameters

Examples

path <- "/home/rishabh/mres/ml_comp/data/"
data <- PrepareData(path, mode = 2, sample = TRUE, size = 100)
mfinal.seq <- seq(20, 25, 5)
Adabag(data, mfinal.seq)

rishi1226/classrish documentation built on May 27, 2019, 9:10 a.m.