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What package is Glmnet in?
The authors of glmnet are Jerome Friedman, Trevor Hastie, Rob Tibshirani, Balasubramanian Narasimhan, Kenneth Tay and Noah Simon, with contribution from Junyang Qian, and the R package is maintained by Trevor Hastie. A MATLAB version of glmnet is maintained by Junyang Qian, and a Python version by B.
What does Glmnet return?
The function glmnet returns a sequence of models for the users to choose from. In many cases, users may prefer the software to select one of them.
Is Lasso better than logistic regression?
Results. Logistic LASSO regression was superior (P<0.05) to SL regression, regardless of whether CDD was included in the covariates, in terms of test misclassification errors (0.234 vs. 0.844, P<0.001), but was comparable to the AUC with CDD (0.873 vs. 0.880, P=0.141).
What is Lambda 1se?
lambda. 1se : largest value of lambda such that error is within 1 standard error of the minimum. Which means that lambda. 1se gives the lambda , which gives an error ( cvm ) which is one standard error away from the minimum error.
What kind of regression models can glmnet fit?
The algorithm is extremely fast, and can exploit sparsity in the input matrix x. It fits linear, logistic and multinomial, poisson, and Cox regression models. It can also fit multi-response linear regression, generalized linear models for custom families, and relaxed lasso regression models.
How to apply Lasso logistic regression with caret?
-Id, data = train.df, method = “glmnet”, trControl = trainControl, metric = “F”, # Optimize by F-measure alpha=1, family=”binomial”) The parameter “alpha” is not recognized, and “the model fit fails in every fold”. What am I doing wrong? Help would be much appreciated. Thanks. Try to use tuneGrid. For example as follows:
How to fit a GLM to lasso regularization?
fit a GLM with lasso or elasticnet regularization Fit a generalized linear model via penalized maximum likelihood. The regularization path is computed for the lasso or elasticnet penalty at a grid of values for the regularization parameter lambda. Can deal with all shapes of data, including very large sparse data matrices.
How is the regularization path computed in glmnet?
Glmnet is a package that fits generalized linear and similar models via penalized maximum likelihood. The regularization path is computed for the lasso or elastic net penalty at a grid of values (on the log scale) for the regularization parameter lambda. The algorithm is extremely fast, and can exploit sparsity in the input matrix x.