Logistic regression via LiblineaR
Source:R/logistic_reg_LiblineaR.R
details_logistic_reg_LiblineaR.RdLiblineaR::LiblineaR() fits a generalized linear model for binary outcomes. A
linear combination of the predictors is used to model the log odds of an
event.
Details
For this engine, there is a single mode: classification
Tuning Parameters
This model has 2 tuning parameters:
penalty: Amount of Regularization (type: double, default: see below)mixture: Proportion of Lasso Penalty (type: double, default: 0)
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.
Translation from parsnip to the original package
logistic_reg(penalty = double(1), mixture = double(1)) %>%
set_engine("LiblineaR") %>%
translate()## Logistic Regression Model Specification (classification)
##
## Main Arguments:
## penalty = double(1)
## mixture = double(1)
##
## Computational engine: LiblineaR
##
## Model fit template:
## LiblineaR::LiblineaR(x = missing_arg(), y = missing_arg(), cost = Inf,
## type = double(1), verbose = FALSE)Preprocessing requirements
Factor/categorical predictors need to be converted to numeric values
(e.g., dummy or indicator variables) for this engine. When using the
formula method via fit(), parsnip will
convert factor columns to indicators.
Predictors should have the same scale. One way to achieve this is to center and scale each so that each predictor has mean zero and a variance of one.
Examples
The “Fitting and Predicting with parsnip” article contains
examples
for logistic_reg() with the "LiblineaR" engine.