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=100000Actual= as.numeric(runif(N) >.5)
Predicted= as.numeric(runif(N))
actual=Actualpredicted=Predicteds1<- 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 overflows3<- 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 overflows5<- 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