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:

- Logistic regression uses a Bernoulli distribution.
- Linear regression uses a Gaussian distribution.