For a square matrix of data, I achieve $R^2=1$ for Linear Regression and $R^2=0$ for Lasso. What's the intuition behind?
Why does Lasso behave "erratically" when the number of features is greater than the number of training instances?
Do I have to remove features with pairwise correlation even if I am doing a regularized logistic regression?
Why is gridsearchCV.best_estimator_.score giving me r2_score even if I mentioned MAE as my main scoring metric?
how Lasso regression helps to shrinks the coefficient to zero and why ridge regression dose not shrink the coefficient to zero?
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