Metrics To Evaluate Machine Learning Algorithms in Python
Last Updated on August 31, 2020
The metrics that you choose to evaluate your machine learning algorithms are very important.
Choice of metrics influences how the performance of machine learning algorithms is measured and compared. They influence how you weight the importance of different characteristics in the results and your ultimate choice of which algorithm to choose.
In this post, you will discover how to select and use different machine learning performance metrics in Python with scikit-learn.
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- Update Jan/2017: Updated to reflect changes to the scikit-learn API in version 0.18.
- Update Mar/2018: Added alternate link to download the dataset as the original appears to have been taken down.
- Update Nov/2019: Improve description of ROC AUC.
- Update Aug/2020: Updated for changes to the API.
Metrics To Evaluate Machine Learning Algorithms in Python
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