#ML_Wednesday 6⃣
Today is a commemoration day, but it doesn't mean that I can take time to read some ML!
Return to the basics for today: the Bias-Variance Tradeoff!
To begin with, here is an interesting math-free introduction to the topic: https://elitedatascience.com/bias-variance-tradeoff
It explains what happens, but not really WHY it happens...
WTF is the Bias-Variance Tradeoff? (Infographic)

Learn the practical implications of the bias-variance tradeoff from this simple infographic, featuring model complexity, under-fitting, and over-fitting.

https://elitedatascience.com/bias-variance-tradeoff
With a little bit more maths to explain the theory behind, another article, by Seema Singh: https://towardsdatascience.com/understanding-the-bias-variance-tradeoff-165e6942b229
(the graphics are around the same)
Understanding the Bias-Variance Tradeoff

Whenever we discuss model prediction, it’s important to understand prediction errors (bias and variance). There is a tradeoff between a…

https://towardsdatascience.com/understanding-the-bias-variance-tradeoff-165e6942b229
Now something very cool I didn't know about... double descent in the test error curve! 🤯🤯
And a very good thread by @daniela_witten about it: https://twitter.com/daniela_witten/status/1292293102103748609
It's very clear and pedagogic so I'll have nothing to add about this topic!
You can follow @MarieSacksick.
Tip: mention @twtextapp on a Twitter thread with the keyword “unroll” to get a link to it.

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