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This is much nicer than weka in my opinion. Less bloat (not java, and doesn't have the full range of algoritms), more emphasis on difficult problems (non-linear
by marshallp 15y ago
This is much nicer than weka in my opinion. Less bloat (not java, and doesn't have the full range of algoritms), more emphasis on difficult problems (non-linear) and practicality (random forests and neural nets and automating everything out). The main creator is working on practical computer vision and not machine learning research directly.
Weka is more suited for teaching than practical work.
- tensor 15y agoThere is a lot that bothers me about this post. First, that a library contains many different algorithms in a single framework is not a negative, nor bloat. Second, there is nothing about random forest or neural nets that are any more practical than maximum entropy or any other learning algorithm. The main researchers are both doing university research work, not building products. I'm not sure where the practical/teaching arguments come in, Weka is used in many different scenarios, including commercial systems. A brief look at the documentation suggests that this package is not nearly as extensive as Weka. Startups might care about the licence being LGPL instead of GPL. I can't comment on convenience and performance without using it a bit, but I've found that other command line driven packages are very easy to use for exploratory type research work.
- brianobush 15y agoI have used Weka in a commercial setting. Usually it is for data analysis and trying many ML techniques. Once I have my data cleaned up and analyzed, I will go an implement my own algorithm (in C). Thus Weka is immensely valuable as a analysis tool.