MachineLearningMetrics.jl

MachineLearningMetrics is a set of tools for quickly scoring models in data science and machine learning. This toolset is written in Julia for blazing fast performance. 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 MachineLearningMetrics:

• Regression metrics (implemented in 0.1.0)
• Classification metrics (implemented in 0.1.0)

The following types of metrics are soon to be implemented in MachineLearningMetrics:

• Multilabel ranking metrics (to be implemented in 0.2.0)
• Clustering metrics (to be implemented in 0.2.0)
• Biclustering metrics (to be implemented in 0.2.0)
• Pairwise metrics (to be implemented in 0.2.0)

Installation

You can install:

• the latest stable release version with

  Pkg.add("MachineLearningMetrics")

• the latest development version from Github with

  Pkg.checkout("MachineLearningMetrics", "dev")

If you encounter a clear bug, please file a minimal reproducible example on Github.

News

MachineLearningMetrics 0.1.0

Improvements

• Implemented functions for scoring regression models.
• Implemented functions for scoring classification models.

API

Load package

using MachineLearningMetrics

Use metrics to score results from models

mean_squared_error([1.0, 2.0], [1.0, 1.0])
accuracy([1, 1, 1, 0], [1, 0, 1, 1])

People

• The original author of MachineLearningMetrics is @Paul Hendricks.

• The lead maintainer of MachineLearningMetrics is @Paul Hendricks.

License