I am a Machine Learning beginner who is currently doing Andrew Ng's Machine Learning course on Coursera. In the lecture, he asked a simple question: Would you call the following a classification or regression problem?

You have a large inventory of identical items. You want to predict how many of these items will sell over the next 3 months

I chose classification since I thought we are just predicting will sell (1) or won't sell (0). But he says it is a regression problem since there are thousands of identical items and it is a continuous output. I don't understand how it is a continuous output. Looks to me like a discrete output having thousands of values. Can someone explain this to me?
Feb 25, 2022 487 views

## 1 answer to this question.

The output will be discrete but the question asked about prediction  has to be understood carefully.

The main difference that makes this a regression problem rather than a classification problem is that the output from the classification is limited to some (usually a few) predefined values ​​/ levels. It's easy to see here that  the sales quantity  can be  any (integer) value in principle, for example it makes no sense  to consider the values ​​14 and 15 as different classes.
It is also helpful to think of the classification output as  a category that is generally unordered. From this point of view, the difference between 14 and 15 can be thought of as the difference between 14 and 764 (these are just different categories). Intuitively, because we are interested in the exact quantity sold, these differences are so different that it is clear that we mis-predicted "14" instead of "15" instead of "764"

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