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Elastic Net alpha value using GLMNET 4.1-8

Is it a valid method to “brute force” the alpha value for an elastic net? What I mean is trying alpha = .1, .2, .3, .4 and so on to 1.0 and looking at the highest R-squared value of each and choosing ...
user162172's user avatar
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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 ...
reuben george's user avatar
2 votes
2 answers
217 views

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 ...
Kengo Ito's user avatar
1 vote
1 answer
77 views

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 ...
Tomer Wolberg's user avatar
1 vote
1 answer
325 views

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 ...
NAS_2339's user avatar
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1 vote
1 answer
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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 ...
NAS_2339's user avatar
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2 votes
1 answer
76 views

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. ...
KiWiChoco's user avatar
0 votes
1 answer
558 views

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 ...
Mohy Mohamed's user avatar
1 vote
0 answers
592 views

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 ...
Oren Matar's user avatar
1 vote
2 answers
779 views

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 ...
Sm1's user avatar
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3 votes
1 answer
199 views

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 ...
mr_python's user avatar
12 votes
3 answers
16k views

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, ...
OldSchool's user avatar
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2 votes
0 answers
299 views

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 ...
yl637's user avatar
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