Need a Machine Learning Algorithm?

July 17, 2019

r entry

The Web site published “101 Machine Learning Algorithms for Data Science with Cheat Sheets.” The write up recycles information from DataScienceDojo, and some of the information looks familiar. But lists of algorithms are not original. They are useful. What sets this list apart is the inclusion of “cheat sheets.”

What’s a cheat sheet?

In this particular collection, a cheat sheet looks like this:

r entry example

You can see the entry for the algorithm: Bernoulli Naive Bayes with a definition. The “cheat sheet” is a link to a python example. In this case, the example is a link to an explanation on the Chris Albon blog.

What’s interesting is that the 101 algorithms are grouped under 18 categories. Of these 18, Bayes and derivative methods total five.

No big deal, but in my lectures about widely used algorithms I highlight 10, mostly because it is a nice round number. The point is that most of the analytics vendors use the same basic algorithms. Variations among products built on these algorithms are significant.

As analytics systems become more modular — that  is, like Lego blocks — it seems that the trajectory of development will be to select, preconfigure thresholds, and streamline processes in a black box.

Is this good or bad?

It depends on whether one’s black box is a dominant solution or platform?

Will users know that this almost inevitable narrowing has upsides and downsides?


Stephen E Arnold, July 17, 2019


One Response to “Need a Machine Learning Algorithm?”

  1. Nathan Piccini on July 22nd, 2019 12:44 pm

    Hi Stephen,

    I just wanted to share the link to the original article with you:


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