Multinomial log-linear learner via neural networks
Source:R/LearnerClassifMultinom.R
mlr_learners_classif.multinom.Rd
Multinomial log-linear models via neural networks.
Calls nnet::multinom()
from package nnet.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
$get("classif.multinom")
mlr_learnerslrn("classif.multinom")
Meta Information
Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “factor”
Required Packages: mlr3, mlr3learners, nnet
Parameters
Id | Type | Default | Levels | Range |
Hess | logical | FALSE | TRUE, FALSE | - |
abstol | numeric | 1e-04 | \((-\infty, \infty)\) | |
censored | logical | FALSE | TRUE, FALSE | - |
decay | numeric | 0 | \((-\infty, \infty)\) | |
entropy | logical | FALSE | TRUE, FALSE | - |
mask | untyped | - | - | |
maxit | integer | 100 | \([1, \infty)\) | |
MaxNWts | integer | 1000 | \([1, \infty)\) | |
model | logical | FALSE | TRUE, FALSE | - |
linout | logical | FALSE | TRUE, FALSE | - |
rang | numeric | 0.7 | \((-\infty, \infty)\) | |
reltol | numeric | 1e-08 | \((-\infty, \infty)\) | |
size | integer | - | \([1, \infty)\) | |
skip | logical | FALSE | TRUE, FALSE | - |
softmax | logical | FALSE | TRUE, FALSE | - |
summ | character | 0 | 0, 1, 2, 3 | - |
trace | logical | TRUE | TRUE, FALSE | - |
Wts | untyped | - | - |
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.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.nnet
,
mlr_learners_regr.ranger
,
mlr_learners_regr.svm
,
mlr_learners_regr.xgboost
Super classes
mlr3::Learner
-> mlr3::LearnerClassif
-> LearnerClassifMultinom
Methods
Method loglik()
Extract the log-likelihood (e.g., via stats::logLik()
from the fitted model.
Examples
if (requireNamespace("nnet", quietly = TRUE)) {
learner = mlr3::lrn("classif.multinom")
print(learner)
# available parameters:
learner$param_set$ids()
}
#> <LearnerClassifMultinom:classif.multinom>
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3learners, nnet
#> * Predict Types: [response], prob
#> * Feature Types: logical, integer, numeric, factor
#> * Properties: loglik, multiclass, twoclass, weights
#> [1] "Hess" "abstol" "censored" "decay" "entropy" "mask"
#> [7] "maxit" "MaxNWts" "model" "linout" "rang" "reltol"
#> [13] "size" "skip" "softmax" "summ" "trace" "Wts"