RapidMiner
From Robin
(Difference between revisions)
(Ny side: == Installation == Download Rapidminer from the [http://rapid-i.com/content/view/26/84/ rapid-i website]. <br> == Using SVM Classifier == The SVM classifier in Rapidminer is based…) |
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Download Rapidminer from the [http://rapid-i.com/content/view/26/84/ rapid-i website]. | Download Rapidminer from the [http://rapid-i.com/content/view/26/84/ rapid-i website]. | ||
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== Using SVM Classifier == | == Using SVM Classifier == | ||
The SVM classifier in Rapidminer is based on LIBSVM. | The SVM classifier in Rapidminer is based on LIBSVM. | ||
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To calculate the best parameters for the classifier, the python script grid.py in the LIBSVM distribution is handy. This is how you install and use it: | To calculate the best parameters for the classifier, the python script grid.py in the LIBSVM distribution is handy. This is how you install and use it: | ||
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*Download [http://www.csie.ntu.edu.tw/~cjlin/libsvm/ LIBSVM] | *Download [http://www.csie.ntu.edu.tw/~cjlin/libsvm/ LIBSVM] | ||
Revision as of 10:27, 21 October 2010
Installation
Download Rapidminer from the rapid-i website.
Using SVM Classifier
The SVM classifier in Rapidminer is based on LIBSVM.
Optimising SVM Parameters
To calculate the best parameters for the classifier, the python script grid.py in the LIBSVM distribution is handy. This is how you install and use it:
- Download LIBSVM
- Install Gnuplot
- Make sure the data is formatted properly. All attributes must be scaled between -1 and 1. The data must be in a text file where each line represents an instance in the classification set. The first value is the correct class, 1:first-attribute 2:second-attribute, and so forth. The example below shows the proper format for two instances of class 1 and two instances of class 2, with four attributes.
1 1:1.000 2:-0.543 3:-0.767 4:-0.253 1 1:-0.184 2:0.144 3:-0.647 4:-0.271 2 1:-0.684 2:-0.542 3:0.723 4:-0.244 2 1:-0.964 2:-1.000 3:0.111 4:-0.472
- Run grid.py with the name of your data file as an argument, and you will get a plot showing the best parameters for the libsvm classifier (C and gamma).
