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Dec 19, 2013 · As an important analytical side note, I interpret getting this warning initially when using Lasso regression as a bad sign, regardless of what happens next. For me it practically always occurred in the situation when the model was over-fitting, meaning that it performed well on the full training set itself, but then poorly during cross-validation and testing.
Sep 23, 2014 · FYI, the maximal penalty yielding nonzero coefficients for the lasso as implemented in sklearn is np.abs(X.T.dot(Y)).max() / len(X). In your case this amounts to 0.0474 . All penalties above this value will yield a coefficient vector equal to 0.
Mar 1, 2016 · I've currently implemented Ridge and Lasso regression using the sklearn.linear_model module. However, the Lasso Regression seems to do 3 orders of magnitude worse on the same dataset! I'm not sure what's wrong, because mathematically, this shouldn't be happening.
Jun 8, 2014 · In Lasso, if you set normalize=True, every column will be divided by its L2 norm (i.e., sd*sqrt (n)) before fitting a lasso regression. The magnitude of design matrix is thus reduced, and the "expected" coefficients will be enlarged. The larger the coefficients, the stronger the L1 penalty.
Nov 28, 2017 · Oct 4, 2021 at 22:18. 1. @AlvaroMartinez Once you get the coefficients, just do this np.array(df.columns)[coeff==0]. This will give you all the features for which Lasso has shrunk the coeff to 0. Similary just replace ==0 with >0 to get features for which Lasso has not shrunk the coeff to 0. – spectre. Jan 9, 2022 at 7:19.
Jan 29, 2019 · 3. First, your question is ill-posed because there exist many algorithms to solve the Lasso. The most popular right now is coordinate descent. Here's the skeleton of the algo (without stopping criterion). I have used numba/jit because for loops can be slow in python. import numpy as np. from numba import njit. @njit.
Mar 5, 2021 · A lasso regression has a unique optimum, but the solver is a sort of gradient descent algorithm, so you'll never actually reach the minimum. tol controls how close you want to be: the smaller tol, the more accurate your final solution will be, but the longer it will take. max_iter controls how many steps you'll take in the gradient descent ...
Oct 3, 2017 · 1. @HerrIvan Yes, if you would pass X = dot (sqrt (diag (weights)), X) and y = dot (sqrt (diag (weights)), y) to the lasso or elasticNet that would be OK to take into account weights. Only problem is that the fit metric used during cross validation would need access to X, y AND your weights to properly calculate the out of sample weighted MSE ...
Jan 13, 2017 · 1 scikit-learn: sklearn.linear_model.LogisticRegression. sklearn.linear_model.LogisticRegression from scikit-learn is probably the best: as @TomDLT said, Lasso is for the least squares (regression) case, not logistic (classification). from sklearn.linear_model import LogisticRegression model = LogisticRegression ( penalty='l1', solver='saga ...
Apr 29, 2019 · ridge_cv.fit(X_train, y_train) This should be the first step to find a good alpha value and/or l1 ratio for your models. Of course other steps as feature engineering and selecting the correct model (f.i. Lasso: performs feature selection) should precede finding good parameters. :) answered Apr 29, 2019 at 14:02.
