Model Training Scripts
Project phase: Machine Learning Model Engineering
You write the reproducible code that trains and tunes your models: a defensible data-splitting and validation strategy, one or more model types, and a documented hyperparameter search rather than defaults. The scripts record learning behaviour well enough to diagnose overfitting and underfitting, fix random seeds and library versions so a rerun reproduces your results, and keep configuration separate from logic so a reviewer can run them on the prepared dataset and get the model and scores you report.
Starting Points
Key Points
- Reproducibility: A reviewer can run your scripts on the provided dataset and get the exact same model and scores that you reported.
- Robust Validation: You have implemented a clear data-splitting strategy (e.g., cross-validation or train/val/test splits) to prove the model generalizes well.
- Hyperparameter Tuning: You have performed a documented search for optimal settings rather than relying on default library parameters.
- Observability: Your scripts record learning behavior (e.g., loss curves) so you can explain why you chose specific regularization or early stopping methods.
- Code Architecture: Configuration (parameters/settings) is separated from the logic (the actual code).
- Consistency: Random seeds are fixed and library versions are specified to prevent "drift" in results.
- Standards: Your approach follows industry standards for machine learning pipelines and is stored in the project repository.