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--- assignments:assignment5 [2015/10/29 14:50]
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+++ assignments:assignment5 [2015/10/30 12:53]
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 Run the L1-SVM on the datasets mentioned above.
-In scikit-learn use ''LinearSVC(penalty='l1', loss='hinge')'' to create one.
+In scikit-learn use ''LinearSVC(penalty='l1', dual=False)'' to create one.
-How many features have non-zero weight vector coefficients?  Compare the accuracy of a regular L2 SVM trained on those features with an L2 SVM trained on all the features computed using 5-fold cross-validation.
+How many features have non-zero weight vector coefficients?  (Note that you can obtain the weight vector of a trained SVM by looking at its ''coef0'' attribute.
+Compare the accuracy of an L1 SVM to an SVM that uses RFE to select relevant features.
-L1-SVMs often leads to solutions that are too sparse.  As a workaround, implement the following strategy:
+Compare the accuracy of a regular L2 SVM trained on those features with an L2 SVM trained on all the features computed using 5-fold cross-validation.
+It has been argued in the literature that L1-SVMs often leads to solutions that are too sparse.  As a workaround, implement the following strategy:
   * Create $k$ sub-samples of the data in which you randomly choose 80% of the examples.
-  * For each sub-sample train an L1-SVM
+  * For each sub-sample train an L1-SVM.
+  * For each feature compute a score that is the average weight vector

CS545 fall 2016