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 0.358