Logistic Regression: Will This Engine Fail?
Back in the Garage: From Numbers to Yes/No Choices In our previous post, we used **Linear Regression** to predict a continuous number: estimating a used car's exact market price based on mileage. But as a master mechanic, you often face a completely different kind of question in the diagnostic bay: "Is this engine going to blow up in the next 10,000 miles? (Yes or No)" Predicting dollar amounts or temperatures requires a straight line. But answering binary questions— Yes or No, Pass or Fail, Fraud or Legitimate, Malignant or Benign —requires a different algorithm entirely: **Logistic Regression**. Why Linear Regression Fails at Yes/No Questions Why can’t we just use a straight line for binary predictions? Imagine setting "No Failure" to 0 and "Engine Failure" to 1 on a graph. If you try to draw a straight linear regression line through this data, the line will inevitably keep going past 1 (predicting a 150% chance of ...