GLM with Elastic Net Regularization Regression Learner
Source:R/LearnerRegrCVGlmnet.R
mlr_learners_regr.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 "gaussian"
.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
$get("regr.cv_glmnet")
mlr_learnerslrn("regr.cv_glmnet")
Meta Information
Task type: “regr”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3learners, 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 | integer | - | \([1, \infty)\) | |
exmx | numeric | 250 | \((-\infty, \infty)\) | |
family | character | gaussian | gaussian, poisson | - |
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 | - | - | |
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 | \([0, \infty)\) | |
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, modified.Newton | - |
type.measure | character | deviance | deviance, class, auc, mse, mae | - |
type.multinomial | character | - | ungrouped, grouped | - |
upper.limits | untyped | - | - |
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/chapters/chapter2/data_and_basic_modeling.html#sec-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.glmnet
,
mlr_learners_regr.kknn
,
mlr_learners_regr.km
,
mlr_learners_regr.lm
,
mlr_learners_regr.nnet
,
mlr_learners_regr.ranger
,
mlr_learners_regr.svm
,
mlr_learners_regr.xgboost
Super classes
mlr3::Learner
-> mlr3::LearnerRegr
-> LearnerRegrCVGlmnet
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("regr.cv_glmnet")
print(learner)
# available parameters:
learner$param_set$ids()
}
#> <LearnerRegrCVGlmnet:regr.cv_glmnet>: GLM with Elastic Net Regularization
#> * Model: -
#> * Parameters: family=gaussian
#> * Packages: mlr3, mlr3learners, glmnet
#> * Predict Types: [response]
#> * Feature Types: logical, integer, numeric
#> * Properties: selected_features, weights
#> [1] "alignment" "alpha" "big"
#> [4] "devmax" "dfmax" "eps"
#> [7] "epsnr" "exclude" "exmx"
#> [10] "family" "fdev" "foldid"
#> [13] "gamma" "grouped" "intercept"
#> [16] "keep" "lambda" "lambda.min.ratio"
#> [19] "lower.limits" "maxit" "mnlam"
#> [22] "mxit" "mxitnr" "nfolds"
#> [25] "nlambda" "offset" "parallel"
#> [28] "penalty.factor" "pmax" "pmin"
#> [31] "prec" "predict.gamma" "relax"
#> [34] "s" "standardize" "standardize.response"
#> [37] "thresh" "trace.it" "type.gaussian"
#> [40] "type.logistic" "type.measure" "type.multinomial"
#> [43] "upper.limits"