As a predictor? about pyhsiclasso HOT 4 CLOSED

I'm not a contributor but I've forked the code and modified it a bit, so I have some understanding. The idea of using HSIC-LASSO is for feature selection. How you use it for classification is up to you. HSIC-LASSO can be used for feature selection for regression or classification problems, however does not act as a classifier on its own, from my understanding. Anyone who knows better, please feel free to correct.

How I've use it is through a two step process: 1) perform feature selection for a classification problem using HSIC-LASSO (such as in this example), and 2) take the selected features and transform your original data (subset your original X matrix to contain only the selected features) and then re-test with a classifier such as those in scikit-learn in order to obtain performance metrics such as precision and accuracy.

There are different ways to input your data and run feature selection (regression vs classification), so check the API (which unfortunately isn't fully or thoroughly documented, but the API code shows pretty clearly how you can use it).

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Sorry for our late reply. @teyden is right, the current implementation does not allow to predict new samples. When we use HSIC Lasso we use the implementation described by @teyden. The only precision is that we favour a classifier that can handle nonlinear relationships, like random forest or kernel SVM.

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Thx, But what a bout Evaluation of selected features ? is there any specific method? What I have done in my thesis for evaluation is the evaluation of classification models such RF,XGB,... under the circumstances of selected subset of features by BHSIC. But I think there should be a specific evaluation process.

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