Running remote host Weka experiments

Previously we have tried to run weka server to utilize all cores of processor in classification tasks. But appears that wekaserver works only in explorer for classification routines. For more advanced machine learning there is more flexible tool – experimenter. Weka server doesn’s support this area. So what to do if you want more performance or simply utilize multi-core processor of local machine. There is a way out, but it is more tricky. Weka has ability to perform remote experiments that allow spreading the load across multiple host machines that have Weka set up. You can read the documentation of remote experiment on Weka wikispaces, but in some cases it may be somewhat confusing. It took time for me to figure out some parts by trial and error. The trickiest part is to set everything up and prepare the necessary command to be run before performing remote experiment. So lets…

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Utilizing multi-core processor for classification in WEKA

Currently WEKA is one of the most favorites machine learning tools. Without programming skills you can do serious classification, clustering and big data analysis. For some time I’ve been using its standard GUI features without thinking much about performance bottlenecks. But since researches are becoming more complex by using ensemble, voting and other meta-algorithms that normally are based on multiple classifiers running simultaneously, the performance issues start becoming annoying. You need to wait for hours until task is completed. The problem is that when running classification algorithms from the WEKA GUI, the utilize a single core of your processor. Such algorithms as Multi-layer Percepron running 10 fold cross-validation is calculating one cross fold at the time on one core taking long time to accomplish: So I started looking for options to make it use all cores of processor as separate threads for each fold of operation. There are couple options…

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Implementing logistic regression learner with python

Logistic regression is a next step from linear regression. The most real life data have non linear relationship, thus applying linear models might be ineffective. Logistic regression is capable of handling hon linear effects in prediction tasks. You can think of lots of different scenarios where logistic regression could be applied. There can be financial, demographic, health, weather and other data where model could be applied and used to predict next events on upcoming data. For instance you can classify emails in to span and non spam, transactions being fraud or non, tumors being malignant or benign. In order to understand logistic regression, let’s cover some basics, do a simple classification on data set with two features and then test it on real life data with multiple features.

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Building and evaluating Naive Bayes classifier with WEKA

This is a followup post from previous where we were calculating Naive Bayes prediction on given data set. This time I want to demonstrate how all this can be implemented using WEKA application. For those who doesn’t know what WEKA is I highly recommend visiting their website and getting latest release. It is really powerful machine learning software written in Java. You can find plenty of tutorials in youtube on how to get started with WEKA. So I wont get in to details. I’m sure you’ll be able to follow anyway.

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