RWKH_framework: RWKH framework This framework does the heavy lifting of...

In rgopikrishnan91/DiscNoise: Framework for Analysis of Impact of Discretization Noise on the Performance and Interpretation of Machine Learning Classifiers

Description

RWKH framework This framework does the heavy lifting of computing the performance and featue importance using out-o-sample boostrap validation for a given data configuration

Usage

RWKH_framework(classifier, data, parallel, n_cores, boot_size, dep_var,
  cutpoint, target, ...)

Arguments

classifier	a string, takes the name of the classifier.Currently supported classifiers are 'rf' - Random forest 'lrm' - Logistic regression 'CART' - Classification tree 'knn' - K-Nearest Neighbors
data	must be a object of type data.frame, with the continuous dependent variable
parallel	a logical value indicating if the function must be executed in parallel –Recommended.
n_cores	a numeric value specifying the number of cores to be used for parallel execution. Defaults to 1.
boot_size	a numeric value. It specifies the number of bootstrap iterations to be used in the framework. Defaults to 100
dep_var	a string giving the column name of continuous dependent variable supplied in the data parameter. This is the variable which creates the discretization noise.
target	a numeric value indicating the amount of discretization noise is to be included relative to cutpoint'

Value

a list, that contains two lists containing performance impacts and importance values for each bootstrap iteration for the classifier

rgopikrishnan91/DiscNoise documentation built on May 6, 2019, 6:59 p.m.