Jason2Brownlee/DataScienceDiagnosticChecklist
Data Science Diagnostic Checklist: Helpful checks for data scientists with urgent problems
This project provides a systematic checklist to diagnose why a machine learning model performs worse on new, unseen data compared to the data it was trained on. It guides you through a series of checks on your data and model, helping you pinpoint issues like data leakage or improper data splits. Data scientists, machine learning engineers, and researchers can use this to troubleshoot and improve the reliability of their predictive models.
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Use this if your machine learning model shows a significant drop in performance when evaluated on a test set or real-world data compared to its performance during training.
Not ideal if you are looking for new model architectures, hyperparameter optimization strategies, or solutions for issues unrelated to generalization gaps or overfitting.
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Dec 05, 2024
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