Simple Models

Simple Models refer to basic, interpretable statistical or machine learning models that prioritize transparency, computational efficiency, and ease of understanding over complex predictive power. They are often used as baseline models, for exploratory data analysis, or in contexts where explainability is critical. Examples include linear regression, decision trees, and naive Bayes classifiers.

Also known as: Basic Models, Interpretable Models, Transparent Models, Baseline Models, Simple ML Models

Why learn Simple Models?

Developers should learn and use Simple Models when starting a machine learning project to establish a performance baseline, for quick prototyping, or in regulated industries like finance or healthcare where model interpretability is required by law. They are also ideal for small datasets, real-time applications with limited computational resources, or when stakeholders need clear insights into how predictions are made.

Learning Resources

• book: Interpretable Machine Learning: A Guide for Making Black Box Models Explainable
• docs: Scikit-learn Documentation: Simple Models Tutorial
• course: Coursera: Machine Learning with Python
• video: StatQuest: Linear Regression Clearly Explained
• tutorial: Towards Data Science: Why Simple Models Often Work Best