Originally published at Programming Tech Lab.
Back in the Garage: From Numbers to Yes/No Choices
In standard Linear Regression, we predict continuous numeric values—such as estimating a used car's exact market price based on mileage. But as a software engineer or data analyst working on diagnostic systems, you often face a completely different question:
"Is this engine going to fail in the next 10,000 miles? (Yes or No)"
Predicting continuous dollar amounts or temperatures requires a straight line. But answering binary classification questions—Yes or No, Pass or Fail, Spam or Ham, Malignant or Benign—requires Logistic Regression.
