Hello, I am currently using linear SVM to perform classification analyses with fMRI data. I wanted to extract the weight for each feature in order to map them back on the brain. Since I am doing a 4 class classification (supposedly under the 'one vs. one' heuristic), there are 3 weights for each feature. I do not come from a math speciality so I wouldn't know how to obtain only 1 weight to map back.
I am also using the RFE and since it just runs several linear SVM and keeps only the best weights to continue, I found that the ordering of the features by weights is done via this line of code: ranks = np.argsort(np.sum(estimator.coef_ ** 2, axis=0)) My question is: Why the summation of the squared weight matrix is used? What is the logic behind it? I would be glad if you could point me to articles or math definitions that would allow me to grasp it. Mathieu Ruiz PhD Student +0033 04 56 52 06 03 Grenoble Institut des Neurosciences (GIN, Centre de Recherche Inserm U 836 ? UJF - CEA - CHU) Centre de Recherche Cerveau et Cognition (CerCo, UMR 5549, CNRS-Université Paul Sabatier Toulouse 3) ------------------------------------------------------------------------------ The demand for IT networking professionals continues to grow, and the demand for specialized networking skills is growing even more rapidly. Take a complimentary Learning@Cisco Self-Assessment and learn about Cisco certifications, training, and career opportunities. http://p.sf.net/sfu/cisco-dev2dev _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
