Day22:House Price Predictor

Linear Regression Deep Dive: House Price Predictor

The sklearn.fit() Trap

Here is how most tutorials teach linear regression:

Four lines. It works. You get coefficients. You move on.

The problem is not that this code is wrong — it is correct. The problem is what it hides. When you call .fit(), sklearn internally constructs a design matrix, solves a least-squares problem using LAPACK routines, optionally applies regularization, and computes residuals. You get back a coef_ array with no indication of whether your features are collinear, whether the solution is numerically stable, or what the uncertainty on any prediction actually is. You cannot see the machinery, so you cannot reason about when it breaks.

In production, linear regression breaks in specific, diagnosable ways: features that are near-duplicates of each other cause the matrix to become singular, giving you coefficients that are numerically meaningless. A model that trains fine in a notebook will silently produce ±$200,000 swings in house price predictions when two input features happen to be correlated at 0.97 in live data. sklearn will not warn you. It will return a number and smile.