Apache Mahout: Scalable machine learning for everyone
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Re: Apache Mahout: Scalable machine learning for everyone
#2Table 1 reminds me why even if these algorithms are available it's a big step to being able to understand and apply them. It's clear the author doesn't have a lot of familiarity with them.
Re: Apache Mahout: Scalable machine learning for everyone
#3Mahout is a great platform, but the real challenge is defining your learning problems, preparing data sets and choosing right algorithms.
Once you are clear as to what you actually want to accomplish chances are you are going to need some kind of significantly modified or hybrid algorithm. Packages like Mahout could help get started, but it is kinda funny that even quite a few examples in this article do not demonstrate actually good algorithm performance, like this one -
Correctly Classified Instances : 41523 61.9219%
Incorrectly Classified Instances : 25534 38.0781%
Total Classified Instances : 67057
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Confusion Matrix
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#4I prefer Weka, mostly because it has excellent literature and has academic leanings, unburdened real-world issues of performance or scalability so it can afford to focus on accuracy.
Re: Apache Mahout: Scalable machine learning for everyone
#5I prefer Weka, mostly because it has excellent literature and has academic leanings, unburdened real-world issues of performance or scalability so it can afford to focus on accuracy.
The real value proposition of Hadoop isn't the algorithms but using Hadoop to massively parallelize the machine learning algorithms. Do you know any port of Weka that can be scaled in such a manner? Just curious.
Re: Apache Mahout: Scalable machine learning for everyone
#6That's cool and stuff, but why do I have to write Java?
Re: Apache Mahout: Scalable machine learning for everyone
#7The Google Prediction API (code.google.com/apis/predict) will help you get started with machine learning without the need to write any additional code (other than API calls)!
Re: Apache Mahout: Scalable machine learning for everyone
#8Honestly, frameworks like Mahout and Weka have their place, and that's typically for exploratory data analysis. My belief is that for large-scale, extremely intensive machine learning, your best bet is to implement algorithms tailored to the job at hand. Algorithms like logistic regression work fine if your data is linearly separable, but it's not a panacea. None of the algorithms are.
If you're interested in machine learning and artificial intelligence, I very strongly consider "enrolling" in Tom Mitchell's machine learning class at http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml -- the lectures are long and the mid-term and final are extremely difficult, but the material covered is an outstanding primer for these types of analyses.
After going through all of the lectures, you will look at things like Mahout and Weka as mere toys, and will be equipped to write your own implementations for whatever task you and your company are working on. It's a lot of front-loading for rewards that may at first glance seem illusory, but investing the time now will pay dividends later.
Re: Apache Mahout: Scalable machine learning for everyone
#9That's cool and stuff, but why do I have to write Java?
Because you want to write in the language the software was written.
Re: Apache Mahout: Scalable machine learning for everyone
#10That's cool and stuff, but why do I have to write Java?
You don't have to. I use mahout from Clojure, not a single line of Java needs to be written.