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Visualizing popular machine learning algorithms

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11–20 of 39 posts

Re: Visualizing popular machine learning algorithms

#11

How is there no training time delay? How is training all these classifiers not putting my CPU into a sweat?? edit: I should also mention: these is very cool :)

The dataset is quite small and you have a fast machine. On my laptop, a 7 year old Core 2, there's a slight delay when running some of the heavier algorithms (e.g. running neural net or svm on the island data set).

Re: Visualizing popular machine learning algorithms

#12
post #4

Looks like K-nearest neighbor does pretty well.

Yes, k-nn is theoretically the one of the best ML algorithms in the sense that it will find the closest items in the training set. For classification or finding similar looking items it is great. However, it has pretty poor running times for evaluation of unseen data ( http://nlp.stanford.edu/IR-book/html/htmledition/time-comple... ). This is contrary to something like neural networks, which take a while to train, bu…

That's why we developed the "Boundary Forest" algorithm which is a fast nearest-neighbor type algorithm with generalization at least as good as K-NN, while being able to respond to queries very quickly.

It maintains trees of examples that let it train and respond to test queries in logarithmic time with the number of stored examples, which can be much less than the overall number of training samples. It thus maintains k-NN's property of very fast training time, and is also an online algorithm, and can be used for regression problems as well as classification.

See our paper that was presented at AAAI 2015 here: http://www.disneyresearch.com/publication/the-boundary-fores...

Re: Visualizing popular machine learning algorithms

#15
post #3

Awesome. Would be great to have execution times. Also what is nerdy.js? I saw it was related to "Carl Edward Rasmussen" but couldn't find another reference on the net

I'm intrigued also. Definitely some sort of Machine Learning related library anyway. Found something related to it but it doesn't really have any substantial information on it either: http://nerdyjs.appspot.com/

Looks like original author is a David Wybiral http://davywybiral.blogspot.ca/2015/10/visualizing-popular-m...

Re: Visualizing popular machine learning algorithms

#19
any visualization of these algorithms in 2 dimensions (with cubic feature expansion!) is completely misleading if you intend to work on any real problem with many dimensions. Also, for those asking for execution times, these would be horribly misleading as well.

Re: Visualizing popular machine learning algorithms

#20
post #4

Looks like K-nearest neighbor does pretty well.

Yes, k-nn is theoretically the one of the best ML algorithms in the sense that it will find the closest items in the training set. For classification or finding similar looking items it is great. However, it has pretty poor running times for evaluation of unseen data ( http://nlp.stanford.edu/IR-book/html/htmledition/time-comple... ). This is contrary to something like neural networks, which take a while to train, bu…

It also suffers from the curse of dimensionality, making it weaker as the number of features increase.
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