assignments:assignment5
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assignments:assignment5 [2015/11/05 09:48] asa [Part 2: Embedded methods: L1 SVM] |
assignments:assignment5 [2015/11/05 09:55] asa [Part 2: Embedded methods: L1 SVM] |
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| In scikit-learn use ''LinearSVC(penalty='l1', dual=False)'' to create one. | In scikit-learn use ''LinearSVC(penalty='l1', dual=False)'' to create one. | ||
| 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. | 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. | + | Compare the accuracy of an L1 SVM to an L2 SVM that uses RFE (with an L2-SVM) to select relevant features. |
| Compare the accuracy of a regular L2 SVM trained on the features selected by the L1 SVM with the accuracy of an L2 SVM trained on all the features (compute accuracy using 5-fold cross-validation). | Compare the accuracy of a regular L2 SVM trained on the features selected by the L1 SVM with the accuracy of an L2 SVM trained on all the features (compute accuracy using 5-fold cross-validation). | ||
assignments/assignment5.txt ยท Last modified: 2016/10/18 09:18 by asa
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