serkor1/SLmetrics
A high-performance R :package: for supervised and unsupervised machine learning evaluation metrics witten in 'C++'.
This project helps data scientists and machine learning engineers quickly and efficiently evaluate the performance of their supervised learning models. It takes actual outcomes and model predictions as input, providing various metrics like Root Mean Squared Error (RMSE) for regression or confusion matrices for classification. This is ideal for R users who need to assess model accuracy and identify areas for improvement.
No commits in the last 6 months.
Use this if you are an R user building and evaluating supervised machine learning models and need fast, memory-efficient performance metrics for both classification and regression tasks.
Not ideal if you primarily work with unsupervised learning models or are not using the R programming language for your machine learning workflows.
Stars
27
Forks
4
Language
C++
License
GPL-3.0
Category
Last pushed
Jun 21, 2025
Commits (30d)
0
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