Cross-Validated GLM with Elastic Net Regularization Survival Learner
mlr_learners_surv.cv_glmnet.Rd
Generalized linear models with elastic net regularization.
Calls glmnet::cv.glmnet()
from package glmnet.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
$get("surv.cv_glmnet")
mlr_learnerslrn("surv.cv_glmnet")
Meta Information
Task type: “surv”
Predict Types: “crank”, “lp”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, glmnet
Parameters
Id | Type | Default | Levels | Range |
alignment | character | lambda | lambda, fraction | - |
alpha | numeric | 1 | \([0, 1]\) | |
big | numeric | 9.9e+35 | \((-\infty, \infty)\) | |
devmax | numeric | 0.999 | \([0, 1]\) | |
dfmax | integer | - | \([0, \infty)\) | |
eps | numeric | 1e-06 | \([0, 1]\) | |
epsnr | numeric | 1e-08 | \([0, 1]\) | |
exclude | untyped | - | - | |
exmx | numeric | 250 | \((-\infty, \infty)\) | |
fdev | numeric | 1e-05 | \([0, 1]\) | |
foldid | untyped | - | ||
gamma | untyped | - | - | |
grouped | logical | TRUE | TRUE, FALSE | - |
intercept | logical | TRUE | TRUE, FALSE | - |
keep | logical | FALSE | TRUE, FALSE | - |
lambda | untyped | - | - | |
lambda.min.ratio | numeric | - | \([0, 1]\) | |
lower.limits | untyped | - , Inf | - | |
maxit | integer | 100000 | \([1, \infty)\) | |
mnlam | integer | 5 | \([1, \infty)\) | |
mxit | integer | 100 | \([1, \infty)\) | |
mxitnr | integer | 25 | \([1, \infty)\) | |
nfolds | integer | 10 | \([3, \infty)\) | |
nlambda | integer | 100 | \([1, \infty)\) | |
offset | untyped | - | ||
parallel | logical | FALSE | TRUE, FALSE | - |
penalty.factor | untyped | - | - | |
pmax | integer | - | \([0, \infty)\) | |
pmin | numeric | 1e-09 | \([0, 1]\) | |
prec | numeric | 1e-10 | \((-\infty, \infty)\) | |
predict.gamma | numeric | gamma.1se | \((-\infty, \infty)\) | |
relax | logical | FALSE | TRUE, FALSE | - |
s | numeric | lambda.1se | \([0, 1]\) | |
standardize | logical | TRUE | TRUE, FALSE | - |
standardize.response | logical | FALSE | TRUE, FALSE | - |
thresh | numeric | 1e-07 | \([0, \infty)\) | |
trace.it | integer | 0 | \([0, 1]\) | |
type.gaussian | character | - | covariance, naive | - |
type.logistic | character | Newton | Newton, modified.Newton | - |
type.measure | character | deviance | deviance, C | - |
type.multinomial | character | ungrouped | ungrouped, grouped | - |
upper.limits | untyped | Inf | - |
References
Friedman J, Hastie T, Tibshirani R (2010). “Regularization Paths for Generalized Linear Models via Coordinate Descent.” Journal of Statistical Software, 33(1), 1--22. doi:10.18637/jss.v033.i01 .
See also
as.data.table(mlr_learners)
for a table of available Learners in the running session (depending on the loaded packages).Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners
mlr3learners for a selection of recommended learners.
mlr3cluster for unsupervised clustering learners.
mlr3pipelines to combine learners with pre- and postprocessing steps.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
Super classes
mlr3::Learner
-> mlr3proba::LearnerSurv
-> LearnerSurvCVGlmnet
Methods
Method selected_features()
Returns the set of selected features as reported by glmnet::predict.glmnet()
with type
set to "nonzero"
.
Arguments
lambda
(
numeric(1)
)
Customlambda
, defaults to the active lambda depending on parameter set.
Returns
(character()
) of feature names.
Examples
learner = mlr3::lrn("surv.cv_glmnet")
print(learner)
#> <LearnerSurvCVGlmnet:surv.cv_glmnet>: Regularized Generalized Linear Model
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3proba, mlr3extralearners, glmnet
#> * Predict Types: [crank], lp
#> * Feature Types: logical, integer, numeric
#> * Properties: selected_features, weights
# available parameters:
learner$param_set$ids()
#> [1] "alignment" "alpha" "big"
#> [4] "devmax" "dfmax" "eps"
#> [7] "epsnr" "exclude" "exmx"
#> [10] "fdev" "foldid" "gamma"
#> [13] "grouped" "intercept" "keep"
#> [16] "lambda" "lambda.min.ratio" "lower.limits"
#> [19] "maxit" "mnlam" "mxit"
#> [22] "mxitnr" "nfolds" "nlambda"
#> [25] "offset" "parallel" "penalty.factor"
#> [28] "pmax" "pmin" "prec"
#> [31] "predict.gamma" "relax" "s"
#> [34] "standardize" "standardize.response" "thresh"
#> [37] "trace.it" "type.gaussian" "type.logistic"
#> [40] "type.measure" "type.multinomial" "upper.limits"