Linear vs. Logistic Regression

In my experience as a manager, it is worthwhile to see how people respond to simple, elementary questions in the field they are in.  These questions are not an indication of a person’s ability to be a competent software engineer but they are a clear sign of how thorough the engineer is, how they respond to a situation where they don’t know the answer, and most importantly, how deeply the person has mastered their field.

So, there is a good probability that when I am interviewing a developer for machine learning or a data scientist, one my questions will be:

What is the difference between linear regression and logistic regression?

The answer to be complete requires the following:

  • Linear regression makes predictions about continuous values (numerical values)
  • Logistic regression makes predictions about binary, discrete values (value pairs such as true or false)
  • Multinomial logistic regression makes predictions about multiclass, discrete values (discrete values of more than 2 classes)
  • Linear regression uses least squares to optimize.
  • Logistic regression uses maximum likelihood to optimize.

Extra credit if the following is given:



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