Start Simple - The Power of Linear Models
Learn how and why to build strong, interpretable baselines: explore linear regression end-to-end, from feature scaling to evaluation, with hands-on notebooks and real data.
Updated May 20, 202513 min read
In the era of complex deep learning architectures, it's easy to overlook the foundational workhorses of machine learning: linear models1. Yet, adhering to the principle of "start simple" - a form of Occam's Razor - is often the most effective strategy. Linear models provide a robust, interpretable, and efficient baseline for many prediction tasks.
This tutorial focuses on the "why" and "how" of linear models, specifically linear regression. We'll cover their importance, the mechanics, essential data preparation steps, and a practical implementation.
Tutorial Goals
- Understand the value proposition of linear models in modern ML
- Understand the core concepts behind linear regression
- Learn essential feature engineering techniques for linear models
- Implement and evaluate a linear regression model using Scikit-learn
- Identify scenarios where starting with a linear model is advantageous