In my last post, I wrote that I am going to work on Machine Learning for my master’s thesis. I am coding an interactive machine learning framework which enables users to run basic/advanced machine learning algorithms online.
In fact, component based frameworks for collecting together data input/output, pre-processing, classification, clustering, regression and visualization schemes and alike have been implemented before in various languages, for use on different platforms, and operated on a variety of data formats. But unfortunately, due to platform depended solutions, it is difficult to try out and compare different machine learning algorithms quickly and easily.
Hopefully, with ML-LAB will provide a sophisticated and easy-to-use wireable interface for creating the workflow. You can upload a dataset, and put a classification algorithm (currently supports K-NN, Naive Bayes and ID3) after it, then wire it to a dimensionality reduction algorithm (PCA, LDA or Isomap), and if you want to, you can wire the results to another algorithm, … It has no connection limits, you can create a workflow with a hundred connections for a single dataset.
The collection of machine learning algorithms are purely implemented in Python and Django is used for interface and matplotlib for the graphics. I’m sharing some screenshots of it, you’ll notice it looks like Yahoo! Pipes a lot. Hopefully, it will be online at www.ml-lab.com after the core library finished.
You can follow ML-Lab on twitter! http://twitter.com/ml_lab
