parrt

7 ms·

31.
A visual explanation for regularization of linear models(explained.ai)
163 points by parrt 6y ago|9 comments
32.
High Performance Numeric Programming with Swift: Explorations and Reflections(fast.ai)
113 points by parrt 8y ago|45 comments
33.
by parrt 8y ago
Just a note that dtreeviz works cross-platform now! Mac, Windows, Linux. "pip install -U dtreeviz" See more at https://github.com/parrt/dtreeviz
34.
by parrt 8y ago
From an educational point of view, I think it's best not to do a survey of models; rather, it's best to pick one and learn all of the stuff surrounding the model first (training, testing, preparing data etc...)
35.
by parrt 8y ago
howdy! We use a pie chart for classifier leaves, despite their bad reputation. For the purpose of indicating purity, the viewer only needs an indication of whether there is a single strong majority category. The viewer does not need to see
36.
by parrt 8y ago
Thanks for that link. Super useful. Looks like BigML uses same layout I did for ANTLR parse trees. Really packs stuff in; e.g., https://cdn-images-1.medium.com/max/1760/1*k0mO4kJyQvPCyyev0...
37.
by parrt 8y ago
Heh that’s a cool idea. Fly through the tree like a maze
38.
by parrt 8y ago
I sat in on this course last Fall. Excellent.
39.
by parrt 8y ago
Yep, the leaves are predictor nodes whereas internal nodes are decision nodes. They are doing different things so we figured we should show them using different visualizations.
40.
by parrt 8y ago
Indeed. They were the inspiration for this visualization. I wanted to do something for my book with Jeremy Howard https://mlbook.explained.ai/ and those guys show the way, but of course it isn't a general library. Lov
41.
by parrt 8y ago
Decision trees are the fundamental building block of gradient boosting machines and Random Forests™, probably the two most popular machine learning models for structured data. Visualizing decision trees is a tremendous aid when learning how
42.
How to visualize decision trees(explained.ai)
343 points by parrt 8y ago|23 comments
43.
Introduction to Machine Learning for Coders(fast.ai)
4 points by parrt 8y ago|2 comments
44.
by parrt 8y ago
Howdy. Agreed. It'd be nice to have a simpler "match x if NOT followed by y" then and something to handle context-sensitive lexical stuff like Python. I often just send all char to the parser and do scannerless parsing. :)
45.
by parrt 8y ago
Well, as the author of ANTLR, I will disagree with your assessment of the tool as you can imagine. It never claimed to be a general tool. Specifically, it cannot handle indirect left-recursion; hence, not a general context free grammar pa
46.
by parrt 8y ago
The key insight seems to be that chasing residuals (for MSE) or sign vectors (for MAE) is chasing a vector (ie direction not just magnitude) and that vector is also a gradient. So chasing residual is performing gradient descent.
47.
by parrt 8y ago
I’m not sure about the connection to category theory. This is mostly an attempt to explain why this model works, that it is performing gradient descent in a particular space. We find that extremely challenging to explain to students. I wou
48.
by parrt 8y ago
The main point of this article is really to explain how gradient boosting works and why. The math is really there to show what the algorithm looks like in its general form. The Discussion of parameters was really just a bit of motivation.
49.
by parrt 8y ago
True, people use a grid search, but I am always very uncomfortable using things as black boxes. How does tree depth affect generality etc...? Effectively using a model means understanding your tools, in my view, but easy to get started w&
50.
by parrt 8y ago
Gradient boosting machines (GBMs) are currently very popular and so it's a good idea for machine learning practitioners to understand how GBMs work. The problem is that understanding all of the mathematical machinery is tricky and, unf
51.
How to explain gradient boosting(explained.ai)
34 points by parrt 8y ago|16 comments
52.
by parrt 8y ago
Gradient boosting machines (GBMs) are currently very popular and so it's a good idea for machine learning practitioners to understand how GBMs work. The problem is that understanding all of the mathematical machinery is tricky and, unf
53.
How to explain gradient boosting(explained.ai)
3 points by parrt 8y ago|1 comments
54.
by parrt 9y ago
Time to revisit any business decision you've ever made based upon default Random Forest feature importances in scikit (Python) or R! Zoiks! :)
55.
by parrt 9y ago
Done. Added a link.
56.
by parrt 9y ago
Added Link to Wolfram Alpha...
57.
by parrt 9y ago
Whoops. thanks. translator error. I'll fix it.
58.
by parrt 9y ago
We have to also adjust the image sizes for the in-line equations. That’s what I need to figure out :)
59.
by parrt 9y ago
Oops. yeah. thanks
60.
by parrt 9y ago
Wow! Great little calculator. Thanks for pointing us at it.

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