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3 votes

Group lasso and feature selection

Presumably, you need a sparse group logistic regression model to perform feature selection while considering the binary response. skglm is a new modular, scikit-...
3 votes
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Elegant way to plot the L2 regularization path of logistic regression in python?

sklearn has such a functionality already for regression problems, in enet_path and lasso_path. There's an example notebook here....
  • 9,962
2 votes
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Do I have to remove features with pairwise correlation even if I am doing a regularized logistic regression?

Yes the L1 regularization will shrink the irrelevant feature coefficients to zero and hence it doesn't require feature selection. In fact it IS a commonly used feature selection technique. So ...
  • 1,293
2 votes
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What's the correct cost function for Linear Regression

Interesting question. I'd say it is correct not to divide, due to the following reasoning... For linear regression there is no difference. The optimum of the cost function stays the same, regardless ...
  • 266
1 vote
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Why is gridsearchCV.best_estimator_.score giving me r2_score even if I mentioned MAE as my main scoring metric?

This is the default behavior for any Scikit-learn regressor, and as far as I know, it cannot be modified. So for regressors, the score method will return the $R^2$ ...
  • 2,266
1 vote

Is it possible to explain why Lasso models eliminated certain coefficient?

Have a look at "Introduction to Statistical Learning" (Chapter 6.2.2). The Lasso adds an aditional penalty term to the original OLS penalty. In addition to the residual sum of squares (RSS, ...
  • 6,952
1 vote
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Accessing regression coefficients when using MultiOutputRegressor

Instead of using the estimator attribute you should be using the best_estimator attribute, after which you can access the ...
  • 6,104

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