ATOMIC: Automated Imbalanced Classification

Description Usage Arguments Value Examples

View source: R/ATOMIC.R

Description

A meta-learning-based AutoML approach to solving imbalanced classification tasks with resampling strategies.

Usage

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ATOMIC(form, train, nmodels = 20, metric = "F1")

Arguments

form

A model formula

train

Training data

nmodels

Number of models to consider. Default is 20. Only such number of top-k models (based on internal validation performance with cross-validation methodology) will be tested.

metric

Evaluation metric used for assessing the optimisation of predictive performance. Default is F1-Score

Value

A predictive model containing the workflow (algorithm+resampling strategy) that are estimated to optimise the generalisation error.

Examples

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## Not run: 

library(mlbench)

data(PimaIndiansDiabetes)

form <- diabetes ~ .

atomic.m <- ATOMIC(form,PimaIndiansDiabetes)


## End(Not run)

nunompmoniz/autoresampling documentation built on July 16, 2020, 1:55 a.m.