The third episode in our Spark series introduced the MLlib library and its Statistics and Exploratory Data Analysis capabilities. This fourth notebook explains how to use the library to build a classifier using Logistic Regression on a large dataset. It also describes two different approaches to model selection.
A good way of using these notebooks is by first cloning the GitHub repo, and then
starting your own IPython notebook in
pySpark mode. For example, if we have a standalone Spark installation
running in our
localhost with a maximum of 6Gb per node assigned to IPython:
MASTER="spark://127.0.0.1:7077" SPARK_EXECUTOR_MEMORY="6G" IPYTHON_OPTS="notebook --pylab inline" ~/spark-1.2.1-bin-hadoop2.4/bin/pyspark
Notice that the path to the
pyspark command will depend on your specific
installation. So as requirement, you need to have
Spark installed in
the same machine you are going to start the
IPython notebook server.
For more Spark options see here. In general it works the rule of passign options
described in the form
We will be using datasets from the KDD Cup 1999.
The following notebooks can be examined individually, although there is a more or less linear ‘story’ when followed in sequence. By using the same dataset they try to solve a related set of tasks with it.
Labeled points and Logistic Regression classification of network attacks in MLlib. Application of model selection techniques using correlation matrix and Hypothesis Testing.
This is an ongoing project. New notebooks will be available soon. The best way to be up to date is to watch our GitHub repo.