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Tyler Hunt thunt@snapfinance.com
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.
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")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 0.358These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.