Interpreting Coefficients in Linear Regression Models
Linear regression models are foundational in machine learning. Merely fitting a straight line and reading the coefficient tells a lot. But how do we extract and interpret the coefficients from these models to understand their impact on predicted outcomes? This post will demonstrate how one can interpret coefficients by exploring various scenarios. We’ll explore the analysis of a single numerical feature, examine the role of categorical variables, and unravel the complexities introduced when these features are combined. Through this exploration, we aim to equip you with the skills needed to leverage linear regression models effectively, enhancing your analytical capabilities across different data-driven domains.
