Introduction
{SLmetrics} is a low-level R package designed for efficient performance evaluation in supervised AI/ML tasks. By leveraging {Rcpp} and {RcppEigen}, it ensures fast execution and memory efficiency, making it ideal for handling large-scale datasets. Built on the robust S3 class system, {SLmetrics} integrates seamlessly with stable R packages, ensuring reliability and ease of use for developers and data scientists alike.

Why?
{SLmetrics} combines simplicity with exceptional performance, setting it apart from other packages. While it draws inspiration from {MLmetrics} in its intuitive design, it outpaces it in terms of speed, memory efficiency, and the variety of available performance measures. In terms of features, {SLmetrics} offers functionality comparable to {yardstick} and {scikit-learn}, while being significantly faster.

Current benchmarks show that {SLmetrics} is between 20-70 times faster than {yardstick}, {MLmetrics}, and {mlr3measures} (See Figure 1).

Whether you’re working with simple models or complex machine learning pipelines, {SLmetrics} provides a highly efficient, reliable solution for model evaluation.

Basic usage of {SLmetrics}
Load {SLmetrics},

library(SLmetrics)

We recode the Species variable and convert the problem to a binary classification problem,

# 1) recode Iris
# to binary classification
# problem
iris$species_num <- as.numeric(
  iris$Species == "virginica"
)

# 2) fit the logistic
# regression
model <- glm(
  formula = species_num ~ Sepal.Length + Sepal.Width,
  data    = iris,
  family  = binomial(
    link = "logit"
  )
)

# 3) generate predicted
# classes
response <- predict(model, type = "response")

# 3.1) generate actual
# classes
actual <- factor(
  x = iris$species_num,
  levels = c(1,0),
  labels = c("Virginica", "Others")
)

Construct the precision-recall curve,

# 4) generate precision-recall
# curve
roc <- prROC(
  actual   = actual,
  response = response
)

Visualize the precision-recall curve,

# 5) plot by species
plot(roc)

Summarise to get the area under the curve metric for each class,

# 5.1) summarise
summary(roc)
#> Reciever Operator Characteristics 
#> ================================================================================
#> AUC
#>  - Others: 0.473
#>  - Virginica: 0.764

The precision-recall function also supports custom thresholds,

# 6) provide custom
# threholds
roc <- prROC(
  actual     = actual,
  response   = response,
  thresholds = seq(0, 1, length.out = 4)
)

Visualize the precision-recall curve with custom thresholds,

# 5) plot by species
plot(roc)

Installing {SLmetrics}
The stable release {SLmetrics} can be installed as follows,

devtools::install_github(
  repo = 'https://github.com/serkor1/SLmetrics@*release',
  ref  = 'main'
)

The development version of {SLmetrics} can be installed as follows,

devtools::install_github(
  repo = 'https://github.com/serkor1/SLmetrics',
  ref  = 'development'
)

Get involved with {SLmetrics}
We’re building something exciting with {SLmetrics}, and your contributions can make a real impact!

While {SLmetrics} isn’t on CRAN yet—it’s a work in progress striving for excellence—this is your chance to shape its future. We’re thrilled to offer co-authorship for substantial contributions, recognizing your expertise and effort.

Even smaller improvements will earn you a spot on our contributor list, showcasing your valuable role in enhancing {SLmetrics}. Join us in creating a high-quality tool that benefits the entire R community. Check out the repository and start contributing today!

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{SLmetrics}: Machine Learning performance evaluation on steroids was first posted on December 5, 2024 at 5:26 pm.
Continue reading: {SLmetrics}: Machine Learning performance evaluation on steroids