assignments:assignment5
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| The L1-SVM is an SVM that uses the L1 norm as the regularization term by replacing $w^Tw$ with $\sum_{i=1}^d |w_i|$. As discussed in class, the L1 SVM leads to very sparse solutions, and can therefore be used to perform feature selection. | The L1-SVM is an SVM that uses the L1 norm as the regularization term by replacing $w^Tw$ with $\sum_{i=1}^d |w_i|$. As discussed in class, the L1 SVM leads to very sparse solutions, and can therefore be used to perform feature selection. | ||
| - | Run the L1-SVM on the datasets mentioned above. 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. | + | Run the L1-SVM on the datasets mentioned above. |
| + | In scikit-learn use ''LinearSVC(penalty='l1', loss='hinge')'' 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. | ||
| L1-SVMs often leads to solutions that are too sparse. As a workaround, implement the following strategy: | L1-SVMs often leads to solutions that are too sparse. As a workaround, implement the following strategy: | ||
assignments/assignment5.txt · Last modified: 2016/10/18 09:18 by asa
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