Combining SVM Classifiers in MapReduce
Make sure that all of the required libraries (scikit-learn, NumPy, pandas) are installed on every node in your cluster.
Your mapper will process each line of input, i.e., your training row and emit a key that basically represents the fold for which you will be training your classifier.
Your reducer will collect the lines for each fold and then run the sklearn classifier on all lines for that fold.
You can then average the results from each fold.
I've been tasked with solving a sentiment classification problem using scikit-learn, python, and mapreduce. I need to use mapreduce to parallelize the project, thus creating multiple SVM classifiers. I am then supposed to "average" the classifiers together, but I am not sure how that works or if it is even possible. The result of the classification should be one classifier, the trained, averaged classifier. 
I have written the code using scikit-learn SVM Linear kernel, and it works, but now I need to bring it into a map-reduce, parallelized context, and I don't even know how to begin.
Any advice?