Comparing supervised learning algorithms

Comparing supervised learning algorithms by Kevin Markham.

From the post:

In the data science course that I instruct, we cover most of the data science pipeline but focus especially on machine learning. Besides teaching model evaluation procedures and metrics, we obviously teach the algorithms themselves, primarily for supervised learning.

Near the end of this 11-week course, we spend a few hours reviewing the material that has been covered throughout the course, with the hope that students will start to construct mental connections between all of the different things they have learned. One of the skills that I want students to be able to take away from this course is the ability to intelligently choose between supervised learning algorithms when working a machine learning problem. Although there is some value in the “brute force” approach (try everything and see what works best), there is a lot more value in being able to understand the trade-offs you’re making when choosing one algorithm over another.

I decided to create a game for the students, in which I gave them a blank table listing the supervised learning algorithms we covered and asked them to compare the algorithms across a dozen different dimensions. I couldn’t find a table like this on the Internet, so I decided to construct one myself! Here’s what I came up with:

Eight (8) algorithms compared across a dozen (12) dimensions.

What algorithms would you add? Comments to add or take away?

Looks like the start of a very useful community resource.

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