Description Usage Arguments Details Value Examples
View source: R/learner.lasso.R
Parallel tuning function using glmnet that is either based on glmnet, boruta or the whole dataset.
1 2 | learner.lasso(data = data_train_numeric_clean_imputed, lasso = FALSE,
boruta = FALSE)
|
data |
Input data which is default set to the numeric, imputed and cleaned training dataset |
lasso |
Boolean flag that shrinks data to the features of |
boruta |
Boolean flag that shrinks data to the features of |
The default execution uses the whole training dataset. By setting either the lasso
or
boruta
parameter to true the number of features is reduced according to the results of
the feature selection. The glmnet learner is wrapper in a Filter wrapper that uses chi squared
as feature selection method. The result is three times cross validated
at maximum 2000 experiments using irace as a control structure.
0 as error output if both flags are set to true
1 | KaggleHouse:::learner.lasso(lasso=TRUE)
|
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