Data Science Stack Exchange
Data Science Stack Exchange

Questions tagged [elastic-net]

12 questions
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What needs to be done to make n_jobs work properly on sklearn? in particular on ElasticNetCV?

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.…
OldSchool
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ElasticNet Convergence odd behavior

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…
mr_python
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I am curious about the interpretation of the elastic Net coefficient

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…
KiWiChoco
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Platt Scaling vs Isotonic Regression for reliability curve

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…
yl637
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How to calculate the significance of each feature?

I built a predictive model using an elastic net regression model with sklearn. The model R2 = 0.015. I know SHAP method could provide the importance of the features. However, How to calculate the significance of each feature? (Get which feature is…
Kengo Ito
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Can elastic net l1 ratio be greater than 1?

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…
Oren Matar
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What is the meaning of the sparsity parameter

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…
Sm1
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Why is the L2 penalty squared but the L1 penalty isn't in elastic-net regression?

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…
Tomer Wolberg
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What is the purpose of positive parameter in sklearn.linear_model.ElasticNet?

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 :…
NAS_2339
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Why are we not checking the significance of the coefficients in Lasso and elastic net models

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…
NAS_2339
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Using BERT for search engine with an Elastic Database

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…
Mohy Mohamed
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Logistic regression with E-net regularization produces different set of weights with each run

I am currently trying to make a model to classify brain tumor patients by incidence of epilepsy using a combination of variables extracted from clinical records, and radiomics features from segmented MR images. In total there are 1396 features…
reuben george
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