I’m excited to announce MLMetrics.jl, a new Julia utility package for scoring models in data science and machine learning. This toolset’s API follows that of Python’s sklearn.metrics as closely as possible so one can easily switch back and forth between Julia and Python without too much cognitive dissonance. The following types of metrics are currently implemented in MLMetrics.jl:
- Regression metrics
- Classification metrics
The following types of metrics are soon to be implemented in MLMetrics.jl:
- Multilabel ranking metrics
- Clustering metrics
- Biclustering metrics
- Pairwise metrics
This package is registered in
METADATA.jl and can be installed as usual
Pkg.add("MLMetrics") using MLMetrics
or check it out on Github here!
mean_squared_error([1.0, 2.0], [1.0, 1.0]) accuracy([1, 1, 1, 0], [1, 0, 1, 1])
You can learn more about MLMetrics.jl on the JuliaML website.
Please let me know if you have any feedback on MLMetrics.jl!