I am trying to apply meachine learning approaches such as: support vector machines, random forests, artificial neural networks on a microRNA dataset (NGS) and also on a mRNA dataset (NGS).I have used sequencing data for training and to make classifications based on some features and then tested on an independent data set to see the prediction accuracy.
To calculate support vector machines (SVM) I used the R e1071 package but I am looking for other tools.
Any body can share/suggest an R/ Python code/Package?
Python provides svm in sklearn. U may use following command:
from sklearn import svm
Link attached gives a description of sklearn svm package
http://scikit-learn.org/stable/modules/svm.html
Python provides SVM packages both for classification or regression. You can explore sklearn that provides many machine learning package in python. If you explore the above link, you can also choose the best SVM model that's tailored to your data characteristics. Have fun with python :)
PyML: http://pyml.sourceforge.net/
mlpy: http://mlpy.sourceforge.net/
scikit-learn: http://scikit-learn.org/stable/
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