100 Days of Machine Learning on Databricks Day 3: Linear vs Logistic Regression

Understanding the Foundations with Real-Life Examples

Before Random Forests, Neural Networks, or Transformers… there was regression.

Even in 2025, Linear and Logistic Regression remain two of the most essential tools in a machine learning practitioner’s toolbox. They’re:

• Interpretable
• Quick to train
• Foundational for understanding more complex models

In today’s article, we’ll compare Linear vs Logistic Regression — with enterprise examples using SAP and Customer 360 data, and break down when and why to use each.

What is Linear Regression?

Linear Regression is a Supervised Learning algorithm used to predict a continuous outcome based on one or more input features.

Think of it as drawing a best-fit line through your data to estimate outcomes.

The Equation:

Where:

• y is the predicted outcome (numeric)
• xi​ are the input features
• βi are the coefficients
• ε is the error term