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:
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: