This learner provides faster fitting procedures for generalized linear models
using the speedglm
package. Arguments are passed to
speedglm.wfit
. Uses glm.fit
as a
fallback if speedglm.wfit
fails.
Lrnr_glm_fast
R6Class
object.
Learner object with methods for training and prediction. See
Lrnr_base
for documentation on learners.
intercept=TRUE
If TRUE
, an intercept term is included
method="Cholesky"
The matrix decomposition method to use
...
Other parameters to be passed to
speedglm.wfit
.
Individual learners have their own sets of parameters. Below is a list of shared parameters, implemented by Lrnr_base
, and shared
by all learners.
covariates
A character vector of covariates. The learner will use this to subset the covariates for any specified task
outcome_type
A variable_type
object used to control the outcome_type used by the learner. Overrides the task outcome_type if specified
...
All other parameters should be handled by the invidual learner classes. See the documentation for the learner class you're instantiating
Other Learners: Custom_chain
,
Lrnr_HarmonicReg
, Lrnr_arima
,
Lrnr_bartMachine
, Lrnr_base
,
Lrnr_bilstm
, Lrnr_condensier
,
Lrnr_cv
,
Lrnr_define_interactions
,
Lrnr_expSmooth
, Lrnr_glmnet
,
Lrnr_glm
, Lrnr_h2o_grid
,
Lrnr_hal9001
,
Lrnr_independent_binomial
,
Lrnr_lstm
, Lrnr_mean
,
Lrnr_nnls
, Lrnr_optim
,
Lrnr_pca
,
Lrnr_pkg_SuperLearner
,
Lrnr_randomForest
,
Lrnr_ranger
, Lrnr_rpart
,
Lrnr_rugarch
, Lrnr_sl
,
Lrnr_solnp_density
,
Lrnr_solnp
,
Lrnr_subset_covariates
,
Lrnr_svm
, Lrnr_tsDyn
,
Lrnr_xgboost
, Pipeline
,
Stack
, define_h2o_X
,
undocumented_learner