I put together an experiment to compare different classification machine learning algorithms in real time to gain insight into which ones handle certain situations better than others: http://jsfiddle.net/wybiral/3bdkp5c0/embedded/result/

Currently the algorithms covered are:

Linear regression (with cubic expansion on feature space)Logistic regression (with cubic expansion on feature space)Neural network (sigmoidal feed forward back propagation)K nearest neighbor (where k=5)Naive bayes Support vector machine (gauss kernel)Discriminate analysis The bottom dropdown has a few simple datasets but you can also click on the classification space to add new blue/orange points.

As the algorithm trains you'll see the background color morph to display the classification boundary. Change the algorithm (via the dropdown on the top) and see how it impacts the classification boundary.

Currently the algorithms covered are:

Linear regression (with cubic expansion on feature space)Logistic regression (with cubic expansion on feature space)Neural network (sigmoidal feed forward back propagation)K nearest neighbor (where k=5)Naive bayes Support vector machine (gauss kernel)Discriminate analysis The bottom dropdown has a few simple datasets but you can also click on the classification space to add new blue/orange points.

As the algorithm trains you'll see the background color morph to display the classification boundary. Change the algorithm (via the dropdown on the top) and see how it impacts the classification boundary.