Visualizing popular machine learning algorithms
jsfiddle.net
Visualizing popular machine learning algorithms
1–10 of 39 posts
Re: Visualizing popular machine learning algorithms
#2Looks like K-nearest neighbor does pretty well.
Re: Visualizing popular machine learning algorithms
#3Awesome. 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
Re: Visualizing popular machine learning algorithms
#4Looks 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, but then evaluate very quickly. For real world use the training times matter to an extent, but in a web app or real time application the latency from knn is just impractical.
Re: Visualizing popular machine learning algorithms
#5I'm a little surprised neural network comes up with a straight line and linear regression doesn't, which I thought by definition it would do. (e.g. on 2 normal groups)
Some discussion of methods, ie how many hidden layers/nodes for the neural network, would probably help make some sense of it.
Random forest could be worth adding.
Re: Visualizing popular machine learning algorithms
#6I'm a little surprised neural network comes up with a straight line and linear regression doesn't, which I thought by definition it would do. (e.g. on 2 normal groups) Some discussion of methods, ie how many hidden layers/nodes for the neural network, would probably help make some sense of it. Random forest could be worth adding.
Looking at the code (http://jsfiddle.net/wybiral/3bdkp5c0/light/) it seems they are expanding the features to include all second and third order terms (options.expansion = cubic), that's why linear regression does not come up with a straight line.
Re: Visualizing popular machine learning algorithms
#7For learners it is confusing to see the nonlinear decision boundaries for linear and logistic regression, IMO a note about the feature expansion should be added
Re: Visualizing popular machine learning algorithms
#8How 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 :)
Re: Visualizing popular machine learning algorithms
#9Awesome. 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:
Re: Visualizing popular machine learning algorithms
#10Wow it is awesome. Can we also play around with classifier parameters ( k of kNN) ?