Body Fat Prediction | 5. Model prediction and evaluation #98

[SrijanShovit]

Is your feature request related to a problem? Please describe.

After all data play, let's delve into actual training part.

Describe the solution you'd like

1. Write generic functions.
2. Use all features; no feature engineering (transformation, selection, extraction, dropping here)
3. Use both normalized and standardized data.
4. Use hyperparameter tuning for models.
5. Use some training strategy like LOSO or K-Fold.
6. Record time to find best params and time to train using those params.
7. Predict on test data and print all eval metrics.
8. If it is binary classification, plot ROC to find optimal threshold.
9. Using that, plot Confusion matrix to evaluate the models.
10. Models to use: Extra Trees, Extra Tree, SVM, Logistic, KNN, DT, RF, GB, Bagging Clf, XGB, Hist GB, MLP, Catboost, Adaboost, Naive Bayes, LightGBM

Describe alternatives you've considered

No response

Additional context

No response

Code of Conduct

• I agree to follow this project's Code of Conduct

[github-actions]

Congratulations, @SrijanShovit! 🎉 Thank you for creating your issue. Your contribution is greatly appreciated and we look forward to working with you to resolve the issue. Keep up the great work!

We will promptly review your changes and offer feedback. Keep up the excellent work! Kindly remember to check our contributing guidelines

[SrijanShovit]

@MehekFatima proceed and can you contact me on discord or linkedin

[MehekFatima]

@SrijanShovit can you share your LinkedIn profile?

[SrijanShovit]

@SrijanShovit can you share your LinkedIn profile?

https://www.linkedin.com/in/srijan-shovit-6b3b131ba/

[github-actions]

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