Vincent Warmerdam: Winning with Simple, even Linear, Models | PyData London 2018
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- čas přidán 9. 07. 2024
- PyData London 2018
Simple models work. Linear models work. No need for deep learning or complex ensembles, you can often keep it simple. In this talk I'll discuss and demonstrate some winning tricks that you can apply on simple, even linear models.
Slides: koaning.io/theme/notebooks/sim...
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“If you understand your solution better than the problem, then you are doing something wrong.” 👏👏
But that sounds like an argument against "simple" models.
I wouldn’t say it’s an argument against simple models. Rather, if you understand the fundamental aspects of your problem then the solution will often avoid unnecessary complexity.
The RBF features tip at 6:10 is really useful for modelling holidays, where the effect isn't exactly binary - thanks Vincent!
00:00 Intro
01:29 Topics to be covered
02:02 XOR problem
04:45 Time-series trick
10:20 Weighted Linear Regression
17:23 Passive-Agressive Algorithms
21:42 Recommender Systems
26:20 Example of Video Games
31:33 Example of Chickens
35:26 Conclusion
Someone should pin this.
You might caption the video, I guess.
This definitely changed my life
one of the best ai presentation out there
One of the best I've seen
This is a great presentation. Keep it simple and also the suits need to understand.
By applying a linear mapping to a non linear basis function, non-linearity can be modeled.
10/10 thank you!
Love it
Hey, do you have any book recommendations to learn exactly this stuff he is talking about? This is really interesting
Not the author, but I may recommend reading "Causal Inference for the Brave and True", with the radial basis functions he was doing a kind of "synthetic control" and maybe finding a resource on splines. For the later portions on machiene learning you can tell he's read "Statistical Rethinking".
@@dangernoodle2868 looks promising, thank you!
I already like this vid at minute 2
Are you on speed? Slow down. Breathe.
This talk is a bit dated. Just because it's easy to understand out-of.the-box, doesn't mean that it is better. I'd rather peer into or interrogate a more accurate deep-learning model to understand how it's working than be satisfied by easy-to-generate plots of a simple and less accurate model.