assignments:assignment5
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| In order for your function to work with the scikit-learn filter framework it needs to have two parameters: ''golub(X, y)'', where X is the feature matrix, and y is a vector of labels. All scikit-learn filter methods return two values - a vector of scores, and a vector of p-values. For our purposes, we won't use p-values associated with the Golub scores, so just return the computed vector of scores twice: if your vector of scores is stored in an array called scores, have the return statement be: | In order for your function to work with the scikit-learn filter framework it needs to have two parameters: ''golub(X, y)'', where X is the feature matrix, and y is a vector of labels. All scikit-learn filter methods return two values - a vector of scores, and a vector of p-values. For our purposes, we won't use p-values associated with the Golub scores, so just return the computed vector of scores twice: if your vector of scores is stored in an array called scores, have the return statement be: | ||
| - | <code python> | + | ''return scores,scores'' |
| - | return scores,scores | + | |
| - | </code> | + | |
| + | |||
| + | ===== Part 2: Embedded methods: L1 SVM ===== | ||
| + | |||
| + | 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. | ||
| + | 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. | ||
| + | Compare the accuracy of an L1 SVM to an SVM that uses RFE to select relevant features. | ||
| + | |||
| + | 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 feature compute a score that is the average weight vector | ||
| - | ===== Part 2: Comparison of filter and embedded methods ===== | ||
| Do your results change if you do model selection for the resulting classifier over a grid of values for the soft margin constant $C$? | Do your results change if you do model selection for the resulting classifier over a grid of values for the soft margin constant $C$? | ||
assignments/assignment5.txt · Last modified: 2016/10/18 09:18 by asa
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