ModelMetrics: Rapid Calculation of Model Metrics
Tyler Hunt [email protected]
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