r descr_models("logistic_reg", "LiblineaR")
defaults <- tibble::tibble(parsnip = c("penalty", "mixture"), default = c("see below", "0")) param <- logistic_reg() %>% set_engine("LiblineaR") %>% make_parameter_list(defaults)
This model has r nrow(param)
tuning parameters:
param$item
For LiblineaR
models, the value for mixture
can either be 0 (for ridge) or 1 (for lasso) but not other intermediate values. In the [LiblineaR::LiblineaR()] documentation, these correspond to types 0 (L2-regularized) and 6 (L1-regularized).
Be aware that the LiblineaR
engine regularizes the intercept. Other regularized regression models do not, which will result in different parameter estimates.
logistic_reg(penalty = double(1), mixture = double(1)) %>% set_engine("LiblineaR") %>% translate()
The "Fitting and Predicting with parsnip" article contains examples for logistic_reg()
with the "LiblineaR"
engine.
Hastie, T, R Tibshirani, and M Wainwright. 2015. Statistical Learning with Sparsity. CRC Press.
Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.
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