feat: Integrate F1 score function to frontend, align with sklearn met…

[muzakkirhussain011]

PR Description

Pull Request - Add F1 Score Function to Ivy Frontend Sklearn Metrics

Overview

This pull request introduces a new feature to the Ivy machine learning framework. It adds an F1 score calculation function, which is a commonly used metric for evaluating the accuracy of a binary classification model.

Details

The F1 score is a measure of a test's accuracy and is the harmonic mean of the precision and recall. The new function is integrated into the frontend metrics of Ivy, aligning with the existing Scikit-learn metrics. This ensures that users familiar with Scikit-learn can easily adapt to using Ivy for their machine learning tasks.

Implementation

• The f1_score function has been implemented in the frontends/sklearn/metrics module.
• It accepts true labels and predicted labels as inputs and calculates the F1 score.
• The implementation is compatible with the Scikit-learn API, making it intuitive for users transitioning from Scikit-learn to Ivy.

Benefits

• Provides a standardized way to evaluate binary classifiers within the Ivy framework.
• Enhances the compatibility of Ivy with Scikit-learn, one of the most popular machine learning libraries.
• Offers users a broader set of tools for model evaluation without leaving the Ivy environment.

Testing

• Comprehensive tests have been added to ensure the accuracy and reliability of the F1 score calculations.
• The tests cover a variety of scenarios

Conclusion

The addition of the F1 score function enriches the Ivy metrics module and bridges the gap between Ivy and Scikit-learn metrics, fostering a more seamless user experience. We welcome feedback and contributions to further refine this feature.

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Looking forward to the community's input on this enhancement!

Closes #

Checklist

• Did you add a function?
• Did you add the tests?
• Did you run your tests and are your tests passing?
• Did pre-commit not fail on any check?
• Did you follow the steps we provided?

[github-actions]

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Issue Reference

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Protected Branch

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[muzakkirhussain011]

Hi @Ishticode ,

I hope this message finds you well. I wanted to bring to your attention the new pull request for the F1 score function that I've submitted. Given your familiarity with the recent precision score PR, which you reviewed and merged, I believe this one will be quite straightforward for you.

The F1 score implementation is similar in structure and follows the same standards we established with the precision score. All backend tests have passed successfully, and I've attached a screenshot of the test outputs for your reference. Additionally, the CI tests are also green across the board.

Could you please review the PR at your earliest convenience? Your insights were invaluable last time, and I'm looking forward to your feedback on this as well.

Thank you for your time and assistance.

Best regards

[muzakkirhussain011]

Hi @Ishticode ,

Could you please review the PR I've submitted on the F1 score? It's related to the previous PR on the precision score that you reviewed. Your timely review will help me proceed with further PRs lined up.

Thank you

[Ishticode]

Looks good. THank you very much @muzakkirhussain011