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I'm interested in using group lasso for a problem I have. Here is a link to the algorithm. I know R has a slick implementation, but am curious to see if python has something similar.
I think sklearn.linear_model.MultiTaskLasso might be kind of similar, but am not sure. Can anyone shed some light on this?
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The group lasso is an extension of the lasso to do variable selection on (predefined) groups of variables in linear regression models. The estimates have the attractive property of being invariant under groupwise orthogonal reparameterizations.
LASSO (Least Absolute Shrinkage and Selection Operator) LASSO is the regularisation technique that performs L1 regularisation. It modifies the loss function by adding the penalty (shrinkage quantity) equivalent to the summation of the absolute value of coefficients.
In contrast, the logistic LASSO regression shrinks such coefficients successively to avoid inflation of the estimated coefficients, which results in superior predictive performance.
The multi-task lasso allows to fit multiple regression problems jointly enforcing the selected features to be the same across tasks. This example simulates sequential measurements, each task is a time instant, and the relevant features vary in amplitude over time while being the same.
I've also looked into this, as far as I know scikit-learn does not provide this implementation.
The MultiTaskLasso does something else. From the documentation:
"The MultiTaskLasso is a linear model that estimates sparse coefficients for multiple regression problems jointly: y is a 2D array, of shape (n_samples, n_tasks). The constraint is that the selected features are the same for all the regression problems, also called tasks."
In other words, the MultiTaskLasso is an implementation of the Lasso which is able to predict multiple targets at the same time (hence y is a 2D array). Another way this problem is known is 'multi-output regression' or 'multi-target regression'. If the tasks are related, such methods can improve methods which try to model every task or target separately.
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