Original posting 8/23/16 by Zohar Strinka on her blog.
A friend asked me for ideas of a good analogy for classification problems in machine learning. In a classification problem, we have a collection of objects, and we somehow want to separate them into groups. Ideally, when designing this analogy there are a few things we want to convey:
The example I suggested is a person deciding what to eat at a potluck. I usually have two different classification problems to worry about when I'm filling my plate. First off, I want to decide which things I want to eat. In addition, I'm one of these people who will get a main course plate, eat that, and then go get dessert later. So as I survey the food, I have to decide both which things I think will be delicious, and which things I want to get later as dessert.
When trying to figure out what will be delicious, there are a lot of criteria I could use. Since I do not like cucumbers, anything with them is immediately excluded. Other properties besides ingredients could be smell, color, how much of it is available, how much was already eaten, the temperature of the food, anything! Some of these criteria are more helpful than others. I've had a lot of delicious brown things in my life at potlucks. And when I am trying to decide if something is dessert of not, how much people ate is not going to be very informative.
How do you classify food at a potluck?
Zohar Strinka is a Data Scientist at Mashey.io. She has a range of interests including optimization, data, auctions, and lean engineering. During her PhD in Industrial and Operations Engineering at the University of Michigan, she focused on using optimization and auctions to solve practical supply chain problems.
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