ModelMetrics: Rapid Calculation of Model Metrics

Tyler Hunt thunt@snapfinance.com

Introduction

ModelMetrics is a much faster and reliable package for evaluating models. ModelMetrics is written in using Rcpp making it faster than the other packages used for model metrics.

Installation

You can install this package from CRAN:

install.packages("ModelMetrics")

Or you can install the development version from Github with devtools:

devtools::install_github("JackStat/ModelMetrics")

Benchmark and comparison

N = 100000
Actual = as.numeric(runif(N) > .5)
Predicted = as.numeric(runif(N))

actual = Actual
predicted = Predicted

s1 <- system.time(a1 <- ModelMetrics::auc(Actual, Predicted))
s2 <- system.time(a2 <- Metrics::auc(Actual, Predicted))
# Warning message:
# In n_pos * n_neg : NAs produced by integer overflow
s3 <- system.time(a3 <- pROC::auc(Actual, Predicted))
s4 <- system.time(a4 <- MLmetrics::AUC(Predicted, Actual))
# Warning message:
# In n_pos * n_neg : NAs produced by integer overflow
s5 <- system.time({pp <- ROCR::prediction(Predicted, Actual); a5 <- ROCR::performance(pp, 'auc')})


data.frame(
  package = c("ModelMetrics", "pROC", "ROCR")
  ,Time = c(s1[[3]],s3[[3]],s5[[3]])
)

# MLmetrics and Metrics could not calculate so they are dropped from time comparison
#        package   Time
# 1 ModelMetrics  0.030
# 2         pROC 50.359
# 3         ROCR  0.358

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Version

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Install

install.packages('ModelMetrics')

Monthly Downloads

105,615

Version

1.2.2.2

License

GPL (>= 2)

Maintainer

Last Published

March 17th, 2020

Functions in ModelMetrics (1.2.2.2)