https://github.com/rbhatia46/classification-metrics
This repository discusses and implements most commonly used classification metrics.
https://github.com/rbhatia46/classification-metrics
Last synced: 7 months ago
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This repository discusses and implements most commonly used classification metrics.
- Host: GitHub
- URL: https://github.com/rbhatia46/classification-metrics
- Owner: rbhatia46
- Created: 2020-07-20T03:27:30.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2020-07-20T03:30:33.000Z (about 5 years ago)
- Last Synced: 2025-01-24T18:37:07.498Z (9 months ago)
- Size: 1000 Bytes
- Stars: 1
- Watchers: 3
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Classification-Metrics
This repository discusses and implements most commonly used classification metrics.
In this repo, we discuss and implement the following classification metrics, which are widely used to evaluate the performance of a ML model for a classification tasks, some of these might be totally new to you and some of which are quite common.
* Accuracy
* Precision
* Recall
* F1-Score
* AUC-ROC
* Log-Loss
* Cohen-Kappa
* Matthew's Correlation Coefficient
All the above metrics are implemented in the code, and you can use this as a boilerplate to understand each metric, along with an implementation for future use.