Cross-Validated GLM with Elastic Net Regularization Survival Learner
Source:R/LearnerSurvCVGlmnet.R
mlr_learners_surv.cv_glmnet.Rd
Generalized linear models with elastic net regularization.
Calls glmnet::cv.glmnet()
from package glmnet.
The default for hyperparameter family
is set to "cox"
.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
mlr_learners$get("surv.cv_glmnet")
lrn("surv.cv_glmnet")
Meta Information
Task type: “surv”
Predict Types: “crank”, “lp”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3learners, glmnet
Parameters
Id | Type | Default | Range | Levels |
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 | list | - | - | - |
exmx | numeric | 250 | \((-\infty, \infty)\) | - |
fdev | numeric | 1e-05 | \([0, 1]\) | - |
foldid | list | NULL | - | - |
gamma | list | - | - | - |
grouped | logical | TRUE | - | TRUE, FALSE |
intercept | logical | TRUE | - | TRUE, FALSE |
keep | logical | FALSE | - | TRUE, FALSE |
lambda | list | - | - | - |
lambda.min.ratio | numeric | - | \([0, 1]\) | - |
lower.limits | list | -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 | list | NULL | - | - |
parallel | logical | FALSE | - | TRUE, FALSE |
penalty.factor | list | - | - | - |
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 | list | 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
Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners
Package mlr3extralearners for more learners.
Dictionary of Learners: mlr_learners
as.data.table(mlr_learners)
for a table of available Learners in the running session (depending on the loaded packages).mlr3pipelines to combine learners with pre- and postprocessing steps.
Extension packages for additional task types:
mlr3proba for probabilistic supervised regression and survival analysis.
mlr3cluster for unsupervised clustering.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
Other Learner:
mlr_learners_classif.cv_glmnet
,
mlr_learners_classif.glmnet
,
mlr_learners_classif.kknn
,
mlr_learners_classif.lda
,
mlr_learners_classif.log_reg
,
mlr_learners_classif.multinom
,
mlr_learners_classif.naive_bayes
,
mlr_learners_classif.nnet
,
mlr_learners_classif.qda
,
mlr_learners_classif.ranger
,
mlr_learners_classif.svm
,
mlr_learners_classif.xgboost
,
mlr_learners_regr.cv_glmnet
,
mlr_learners_regr.glmnet
,
mlr_learners_regr.kknn
,
mlr_learners_regr.km
,
mlr_learners_regr.lm
,
mlr_learners_regr.ranger
,
mlr_learners_regr.svm
,
mlr_learners_regr.xgboost
,
mlr_learners_surv.glmnet
,
mlr_learners_surv.ranger
,
mlr_learners_surv.xgboost
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
if (requireNamespace("glmnet", quietly = TRUE)) {
learner = mlr3::lrn("surv.cv_glmnet")
print(learner)
# available parameters:
learner$param_set$ids()
}
#> <LearnerSurvCVGlmnet:surv.cv_glmnet>
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3proba, mlr3learners, glmnet
#> * Predict Type: crank
#> * Feature types: logical, integer, numeric
#> * Properties: selected_features, weights
#> [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"