Machine Learning Best Practices

What this best practices guide is

Best practices for building and evaluating a machine learning model before it goes anywhere near production, checked against the scikit-learn and PyTorch documentation, Google's Rules of Machine Learning and ML Crash Course, MLflow and Fairlearn. It covers problem framing and baselines, data splits and leakage, features, training and tuning, metrics and error analysis, reproducibility and experiment tracking, and fairness checks.

It has 7 chapters and 30 practices, each with why it matters, what to do and what to avoid. For developers and data scientists training models.

Chapter by chapter

1. Problem framing and baselines (4 practices)

Decide what the model predicts, what decision it supports and what it has to beat before you train anything. These practices set the bar every later chapter measures against.

2. Data splits and leakage (5 practices)

Leakage makes a weak model look strong. These practices, mostly from scikit-learn's common pitfalls guide, keep information from the evaluation data out of training.

3. Features (4 practices)

Good features matter more than clever models, especially early on. These practices come from Google's Rules of ML and scikit-learn's preprocessing tools.

4. Training and tuning (4 practices)

Tuning is where leakage most often sneaks back in. These practices keep hyperparameter search honest and training runs comparable.

5. Evaluation metrics and error analysis (5 practices)

A single headline number rarely tells you whether a model is good enough. These practices pick the right metric and look past the average.

6. Reproducibility and experiment tracking (4 practices)

If you cannot rerun an experiment, you cannot tell whether a change helped. These practices control randomness and record what each run did.

7. Fairness checks (4 practices)

A model can perform well on average and still fail some groups of people. These practices follow Fairlearn's assessment guide.

How it works on Sefism

The guide reads like a short course: a syllabus, then one chapter per page in a focused reader, with a quiz at the end of each chapter for members. The opening chapters are open to everyone. Adding it to your account uses credits, opens every chapter and keeps your progress.

Every claim is checked against the sources listed on the page (8 cited).