The purpose of Mahout is machine learning. It is basically meant for machine learning with its machine learning framework. But when it integrates itself with Hadoop, it gives a wide flexibility and power to deal with huge data-sets and implements the algorithms based on the data.
Difference between Normal and Machine Learning Algorithm
Machine Learning is a class of algorithms that are data-driven. The difference is that in a normal algorithm, you are the one who would write a program right from specifications. If you are supposed to write a non-machine learning algorithm, means a normal program for identifying a human face, then you will specify some criteria, like the face should look like this, eyes should be in this position, nose at this particular place, colour should vary from this to this, and so on. Based on these criteria, you will feed the data to the program and based on these instructions and the rules that you have defined, the program will give you the output that whether the input is a human face or not. This is what happens with a normal, non-machine learning program.
When it comes to a data-driven algorithm or a machine learning algorithm, the program simply doesn’t know what the rules to identify a human face are. So, at the time of execution, these rules are not even defined. This is the difference, and this is what learning by examples is. If you talk about Mahout implementation of the same problem statement, what Mahout will do is processing the example data to give out the results.
How does it work?
With the application of alternative classification kind of algorithm, you’ll have to feed multiple ages. Based on the huge amount of datasets, along with multiple examples, which will come, it would eventually be able to predict whether or not the next image you are feeding to the program is a human face. Let’s say there are many faces, like human face, animal face, bird face, etc. In such a case, you in your program need not write it. It would be your Mahout program or the algorithm which will write, based on the data it has, and it will send you the appropriate category that the particular face falls in, be it human face, or any other. Machine learning enables you to define the rules, and feed the data, while in a normal algorithm, you find out the rules based on data itself. That’s the kind of power Mahout gives you.
In LinkedIn, when you add a particular person in your network, the machine doesn’t know anything at that time and there are no rules to guide it. On the basis of your educational qualifications, skills and interests, past experience, and several other factors, it recommends the kind of companies you may want to follow and the kind of jobs you may take interest in. Mahout finds its quite a good use in neural networks. Now, continuing on the same lines, when it comes to Mahout, most of the websites, especially the websites which are dealing with video generated content, classification, representation, etc. apply Mahout implementations.
Is Mahout all about Machine Learning?
No, Mahout is about Machine Learning at a huge scale of data. It can scale up easily. Why it can scale up so easily is because it can take the leverage of Hadoop, MapReduce. You can implement Mahout Algorithms over MapReduce. You can use the algorithms to process huge amounts of data and generate the meaningful insights.
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