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SrijanShovit / HealthLearning

A repo comprising of various Machine Learning and Deep Learning projects in healthcare domain.
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Body Fat Prediction | 5. Model prediction and evaluation #98 #100

Closed SrijanShovit closed 6 months ago

SrijanShovit commented 6 months ago

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

github-actions[bot] commented 6 months ago

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 commented 6 months ago

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

MehekFatima commented 6 months ago

@SrijanShovit can you share your LinkedIn profile?

SrijanShovit commented 6 months ago

@SrijanShovit can you share your LinkedIn profile?

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

github-actions[bot] commented 6 months ago

This issue has been automatically closed because it has been inactive for more than 7 days. If you believe this is still relevant, feel free to reopen it or create a new one. Thank you!