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Apache Mahout: Scalable machine learning for everyone

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Re: Apache Mahout: Scalable machine learning for everyone

#11

Post by tensor »

law wrote: post #8

Honestly, 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…

Libraries like Weka and Mahout are no more toys than any other library that implements standard and widely applicable algorithms. Yes, you need to do a lot of extra work to properly model your problems, choose features, and combine different algorithms into a final product. But it's not often that you need to tweak the core algorithms that these libraries provide.

If you really understand enough to implement new classifiers or other types of learning algorithms, these libraries are still useful to you. For one, they provide a solid framework for allowing your new algorithm to easily interact with other algorithms. Two, it's not unlikely that your new algorithm is a variation on an existing one. Don't re-implement it. These libraries are open, so copy the source and modify it. And three, mahout uses hadoop. Distributed processing systems are another topic altogether. If you are proposing to write your own, I would hope that you have good reasons for spending the time. Hadoop is certainly no toy.

In summary, don't waste time reimplementing core algorithms unless you are doing it for a learning exercise. But do still take a good course on machine learning, because using the provided algorithms in these packages and others correctly is highly non-trivial.

Re: Apache Mahout: Scalable machine learning for everyone

#12

Post by dvcat »

tensor wrote: post #11
law wrote: post #8

Honestly, 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…

Libraries like Weka and Mahout are no more toys than any other library that implements standard and widely applicable algorithms. Yes, you need to do a lot of extra work to properly model your problems, choose features, and combine different algorithms into a final product. But it's not often that you need to tweak the core algorithms that these libraries provide. If you really understand enough to implement new clas…

Dunno about weka but my last experience ( 5 months back) with Mahout was not good. There still are quite a few bugs and the fact that entire code base is in Java makes it extremely unpleasant for someone who wants to hack and modify the code to jump right in and start tweaking stuff. However, in its defense, it is open source is probably the only hadoopified ml library out there and has given me a ton of good ideas on how to write custom code.

Re: Apache Mahout: Scalable machine learning for everyone

#13

Post by law »

tensor wrote: post #11
law wrote: post #8

Honestly, 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…

Libraries like Weka and Mahout are no more toys than any other library that implements standard and widely applicable algorithms. Yes, you need to do a lot of extra work to properly model your problems, choose features, and combine different algorithms into a final product. But it's not often that you need to tweak the core algorithms that these libraries provide. If you really understand enough to implement new clas…

You're correct to identify the point of libraries like Weka and Mahout, which are both written in Java, as providing a solid framework for interaction between and among your program and other algorithms. However, Java isn't the right solution for everyone. Moreover, in Weka's case, the GPL licensing may not comport with everyone's requirements. Mahout's license is more friendly to proprietary software, so it's admittedly a non-issue there.

I agree that hadoop is certainly not a toy, but using Mahout on hadoop clusters works better for analyzing large data sets that you've already collected and pre-processed. If you're doing any kind of active learning, or are designing software to run on a client's computer based on feedback that they provide, mahout probably isn't the best choice.

In the end, it requires understanding your problem completely enough to justify your decision.

Re: Apache Mahout: Scalable machine learning for everyone

#14

Post by _delirium »

law wrote: post #8

Honestly, 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…

There aren't very many statisticians/MLers who suggest (or practice) reimplementing your own algorithms, except for quite simple things, because the risk of getting something wrong is pretty high, and the work to make things efficient is non-trivial. If anything, the current push is in the other direction, towards encouraging more people to share their code, and more people to use well-tested code, through initiatives like http://jmlr.csail.mit.edu/mloss/ , http://www.jstatsoft.org/ , and CRAN.

For example, you could reimplement your own SVM instead of using http://svmlight.joachims.org/ , but your chance of producing something correct and as efficient is pretty low...

Re: Apache Mahout: Scalable machine learning for everyone

#15

Post by law »

_delirium wrote: post #14
law wrote: post #8

Honestly, 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…

There aren't very many statisticians/MLers who suggest (or practice) reimplementing your own algorithms, except for quite simple things, because the risk of getting something wrong is pretty high, and the work to make things efficient is non-trivial. If anything, the current push is in the other direction, towards encouraging more people to share their code, and more people to use well-tested code, through initiative…

It's a fine line to walk. On the one hand, community-vetted code is a spectacular idea for the core algorithms, but on the other, overly-restrictive licenses (like [L]GPL) effectively preclude the maximum utility being derived from them.

Re: Apache Mahout: Scalable machine learning for everyone

#17

Post by fauigerzigerk »

_delirium wrote: post #14
law wrote: post #8

Honestly, 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…

There aren't very many statisticians/MLers who suggest (or practice) reimplementing your own algorithms, except for quite simple things, because the risk of getting something wrong is pretty high, and the work to make things efficient is non-trivial. If anything, the current push is in the other direction, towards encouraging more people to share their code, and more people to use well-tested code, through initiative…

I think the choice between using existing libraries and implementing your own mostly depends on how central particular algorithms are to your product. If a better algorithm makes a great difference for my customers then it's insane for me to use an existing library.

I don't even find much value in looking at existing code as a starting point because it's bound to be either obscured by lots of optimizations or naive or it's university code left behind by someone finishing their thesis in a hurry. For code beyond a certain level of complexity I prefer to either use it as a black box or implement it myself.

Obviously, if the algorithm is not a core component of my product it's insane to waste time on reimplementing it, provided there is a good quality implementation that has the right license.

Re: Apache Mahout: Scalable machine learning for everyone

#18

Post by Radim »

dmk23 wrote: post #3

Mahout 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 demonstr…

There are decimal dots missing in the confusion matrix numbers (i.e., 190440 should read 19044.0, in case anyone else was wondering why the numbers don't add up).

If anything, the article convinced me not to use Mahout. So, the author decided to use the simplest algorithm, Naive Bayes, and got miserable results (from the article: "This is possibly due to a bug in Mahout that the community is still investigating."). He then changed to problem formulation in order to get better results, and concluded by saying the outcome is still likely a bug, but he's happy with it anyway?

This would be probably fine if we were talking about a small, nimble project that you could go into and hack/fix yourself. But we're talking about a massive, Java codebase. The thought of customizing it makes me shudder.

EDIT: forgot to mention I agree with the parent comment completely, except I would add "... and choosing the right evaluation process" to the initial sentence.

Re: Apache Mahout: Scalable machine learning for everyone

#19

Post by mwexler »

tel wrote: post #2

Table 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.

He is co-founder of the Mahout project with a pretty extensive background in text analysis. I suspect he's familiar with the algorithms. In fact, he may be showing the reader that they _aren't_ as magical as one may believe, by showing that they don't work perfectly oob.

Unless you are being sarcastic, in which case, forgive me for missing it.

Re: Apache Mahout: Scalable machine learning for everyone

#20

Post by mark_l_watson »

Another good article by Grant Ingersoll on Mahout. I used Mahout on a customer project last year when it was not yet a complete machine learning system layered on Hadoop. Looking at Table 1. in this article, many of the previous gaps have been implemented. BTW, the book Mahout in Action is a good guide but the new MEAP released last week does not cover some of the new features, which is OK. Also, Grant has been working on "Taming Text" for a while, but a new MEAP has not been released in a year or two - I would bet that his energies have been focused on extending and using Mahout.
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