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Description

Rapid Calculation of Model Metrics.

Collection of metrics for evaluating models written in C++ using 'Rcpp'. Popular metrics include area under the curve, log loss, root mean square error, etc.

ModelMetrics: Rapid Calculation of Model Metrics

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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
Metadata

Version

1.2.2.2

License

Unknown

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