There was some data set I worked with which I wanted to solve non negative least squares (NNLS) on and I wanted a sparse model. After a bit of experiementing I found that what worked the best for me was using the following loss function: $$\min_{x \geq 0} ||Ax-b|| + \lambda_1||x||_2^2+\lambda_2||x||_1^2$$ Where the L2 squared penalty was implemented by adding white noise with a standard deveation of $\sqrt{\lambda_1}$ to $A$ (which can be showed to be equivelent to ridge regression …

As far as I know, we don't check the coefficient significance in Lasso and elasticnet models. Is it because insignificant feature coefficients will be driven to zero in these models?. Does that mean that all the features in these models are significant? Why are we not checking the significance of the coefficients in Lasso and net elastic models?.

I saw this parameter in the sklearn.linear_model.ElasticNet. What is the purpose of this? What is the possible scenario where we want to force the coefficients to be positive? How is this achieved? Doesn't it affect model performance? positive : bool, default=False When set to True, forces the coefficients to be positive.

I have a small dataset with just 160 data points. When I tried ordinary linear regression on the data, I could not add more than four features without vif inflating greater than 5 (I made a stepwise forward feature selection). I decided to try an elastic net model on it. The elastic net model with all the possible features on one go gave me more features in the model? (non zero coefficients). Does this mean that all my coefficients are …

I am optimizing a model using ElasticNet, but am getting some odd behavior. When I set the tolerance hyperparameter with a small value, I get "ConvergenceWarning: Objective did not converge" errors. So I tried a larger tolerance value, and the convergence error goes away, but now the test data consistently gives a higher root mean squared error value. This seems backwards to me, if the model does not converge, what can cause it to give a better RMSE score, or …

Sparse methods such as LASSO contain a parameter $\lambda$ which is associated with the minimization of the $l_1$ norm. Higher the value of $\lambda$ ($>0$) means that more coefficients will be shrunk to zero. What is unclear to me is that how does this method decides which coefficients to shrink to zero? If $\lambda = 0.5$ then does it mean that those coefficients whose values are less than or equal to 0.5 will become zero? So in other words, whatever …

I want to discover the importance of variables in data through sklearn's Elactic Net. But I don't understand the exact meaning of coefficient. When training, I used alpha: 0.01585598, l1_ratio: 1.000. The graph below is a coefficient graph drawn from my data. My goal is to predict "Time Spend" through various variables. Please understand that the column names are marked with A, B, C, and D due to personal information. In the graph, what does variable "A" mean for coefficients …

I want to make Documents search engine where the user will type a query and top n relevant documents should be shown. I want to use BERT for the searching and the first question is can i use it with an Elastic Database ? Seconed question is which task should i use for the pretrained model 1) Question-Answer 2) Binary Classification as 1 relevant 2 not relevant ?

I have multiple datasets that I trained with ElasticNetCV (sklearn), and I noticed that many of them selected l1_ratio = 1 as the best value (which is the max value tried by the CV), So as a test I wondered if values greater than 1 will produce a better result - and surprisingly the answer is yes... in fact you can reproduce this phenomenon with this code: from sklearn.linear_model import ElasticNet from sklearn.model_selection import train_test_split n = 200 features = …

The constructor of sklearn.linear_model.ElasticNetCV takesn_jobs as an argument. Quoting the documentation here n_jobs: int, default=None Number of CPUs to use during the cross validation. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details. However, running the below simple program on my 4 core machine (spec details below) shows performance is best when n_jobs = None, progressively deteriorating as you increase n_jobs all the way to n_jobs = -1 (supposedly requesting all …

I am learning classifier probability calibrations and have calibrated an eleastic net model using both Platt scaling and isotonic regression. As you can see in the attached image Platt scaling (on the bottom) better approximates the diagonal line compared to isotonic regression (top), however I noticed I am losing information with any predictions where the predicted probability <0.4, I have seen this happen in uncalibrated plots as well. Therefore I am wondering which calibration method I should be using. Furthermore, …