Single Layer Neural Network.
Calls nnet::nnet.formula()
from package nnet.
Note that modern neural networks with multiple layers are connected via package mlr3keras.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
$get("classif.nnet")
mlr_learnerslrn("classif.nnet")
Meta Information
, * Task type: “classif”, * Predict Types: “prob”, “response”, * Feature Types: “numeric”, “factor”, “ordered”, * Required Packages: mlr3, mlr3learners, nnet
Parameters
, |Id |Type |Default |Levels |Range |, |:---------|:-------|:-------|:-----------|:------------------------------------|, |Hess |logical |FALSE |TRUE, FALSE |- |, |MaxNWts |integer |1000 | |\([1, \infty)\) |, |Wts |untyped |- | |- |, |abstol |numeric |1e-04 | |\((-\infty, \infty)\) |, |censored |logical |FALSE |TRUE, FALSE |- |, |contrasts |untyped | | |- |, |decay |numeric |0 | |\((-\infty, \infty)\) |, |mask |untyped |- | |- |, |maxit |integer |100 | |\([1, \infty)\) |, |na.action |untyped |- | |- |, |rang |numeric |0.7 | |\((-\infty, \infty)\) |, |reltol |numeric |1e-08 | |\((-\infty, \infty)\) |, |size |integer |3 | |\([0, \infty)\) |, |skip |logical |FALSE |TRUE, FALSE |- |, |subset |untyped |- | |- |, |trace |logical |TRUE |TRUE, FALSE |- |
References
Ripley BD (1996). Pattern Recognition and Neural Networks. Cambridge University Press. doi:10.1017/cbo9780511812651 .
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.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
-> LearnerClassifNnet
Examples
if (requireNamespace("nnet", quietly = TRUE)) {
learner = mlr3::lrn("classif.nnet")
print(learner)
# available parameters:
learner$param_set$ids()
}
#> <LearnerClassifNnet:classif.nnet>
#> * Model: -
#> * Parameters: size=3
#> * Packages: mlr3, mlr3learners, nnet
#> * Predict Type: prob
#> * Feature types: numeric, factor, ordered
#> * Properties: multiclass, twoclass, weights
#> [1] "Hess" "MaxNWts" "Wts" "abstol" "censored" "contrasts"
#> [7] "decay" "mask" "maxit" "na.action" "rang" "reltol"
#> [13] "size" "skip" "subset" "trace"