How to understand the format type of libsvm of Spark MLlib?
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I am new for learning Spark MLlib. When I was reading about the example of Binomial logistic regression, I don't understand the format type of "libsvm". (Binomial logistic regression)

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Can you help me to understand the format type of libsvm of Spark MLlib? Thanks!

Burkle answered 7/7, 2017 at 7:39 Comment(1)
Possible duplicate of How can I read LIBSVM models (saved using LIBSVM) into PySpark?Lilywhite
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The LibSVM format is quite simple. The first row contains the class label, in this case 0 or 1. Following that are the features, here there are two values for each one; the first one is the feature index (i.e. which feature it is) and the second one is the actual value.

The feature indices starts from 1 (there is no index 0) and are in ascending order. The indices not present on a row are 0.

In summary, each row looks like this;

<label> <index1>:<value1> <index2>:<value2> ... <indexN>:<valueN>

This format is advantageous to use when the data is sparse and contain lots of zeroes. All 0 values are not saved which will make the files both smaller and easier to read.

Burgas answered 7/7, 2017 at 7:52 Comment(2)
Thank you for your help. I know about the label and the actual value, But I don't understand about the feature indices and I think it is useless.Burkle
If the data is sparse this format is quite good, as you can just ignore all 0's in the data. If you don't use indices in the example you posted you need over a 100 0's before the first actual value. It also makes the data a lot easier to read.Burgas

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