EDUCATION & TRAINING
Your Regression Model Lies When X Doesn't Vary — And You Probably Don't Notice
Dev.to Machine Learning
About This Tutorial
There's a classic trap in data science projects with linear regression that catches a lot of people: the model trains, the loss looks fine, the R² even seems reasonable - but the coefficient estimates are a mess. The reason, almost always, is simple: X doesn't vary enough. The Problem in 30 Seconds In simple linear regression: Y = β₀ + β₁X + ε The variance of the estimated coefficient is: Var(β̂₁) = σ² / Σ(xᵢ - x̄)² Read it as: Var(β̂₁) = model noise / variation in X Two direct