How can I use the Lasso to apply to Logistic Regression?
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I am trying to apply supervised binary classification problem with the help of lasso to prevent overfitting. But I am stuck at how to apply lasso to logistic classification function, and how to predict the response values.
Below is the code, where:
- grpTrain_Lasso: a vector of values 1's & 2's, representing 2 categories.
- grpTrain_Lasso_categorical: containing 2 categories: "Cancer", "Normal".
- grpTrain: Original categorical vector, containing the diagnosis of each patient. ("Cancer", "Normal")
- obsSmall: 195x100, where columns are # of patients records, rows are # of features variables.
Lasso Embedded Model Training
[grpTrain_Lasso grpTrain_Lasso_categorical] = grp2idx(grpTrain)
lModel = lasso(obsSmall, grpTrain_Lasso, "CV", 20)
% column: predictor
% row: lambda value for each parameter (for the predictor)
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