Utility package for scoring models in data science and machine learning. This toolset is written in Julia for blazing fast performance.
Package Status | Package Evaluator | Build Status |
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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
:
- Regression metrics
- Classification metrics
The following types of metrics are soon to be implemented in MLMetrics
:
- 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
If you encounter a clear bug, please file a minimal reproducible example on Github.
mean_squared_error([1.0, 2.0], [1.0, 1.0])
accuracy([1, 1, 1, 0], [1, 0, 1, 1])
This code is free to use under the terms of the MIT license.
The original author of MLMetrics
is @Paul Hendricks.