Multinomial log-linear learner via neural networks

Multinomial log-linear models via neural networks. Calls nnet::multinom() from package nnet.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("classif.multinom")
lrn("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/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr3::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

Public methods

• LearnerClassifMultinom$new()

• LearnerClassifMultinom$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$selected_features()
• mlr3::Learner$train()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifMultinom$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifMultinom$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("nnet", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("classif.multinom")
print(learner)

# Define a Task
task = tsk("sonar")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

# importance method
if("importance" %in% learner$properties) print(learner$importance)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
}
#> 
#> ── <LearnerClassifMultinom> (classif.multinom): Multinomial Log-Linear Model ───
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3learners, and nnet
#> • Predict Types: [response] and prob
#> • Feature Types: logical, integer, numeric, and factor
#> • Encapsulation: none (fallback: -)
#> • Properties: multiclass, twoclass, and weights
#> • Other settings: use_weights = 'use'
#> # weights:  62 (61 variable)
#> initial  value 96.347458 
#> iter  10 value 29.798498
#> iter  20 value 11.198546
#> iter  30 value 5.075010
#> iter  40 value 0.275305
#> iter  50 value 0.002855
#> final  value 0.000057 
#> converged
#> Call:
#> nnet::multinom(formula = Class ~ ., data = task$data())
#> 
#> Coefficients:
#> (Intercept)          V1         V10         V11         V12         V13 
#>  1665.10612 -1881.84170 -1012.45299 -1544.70196 -1579.26284  -237.57831 
#>         V14         V15         V16         V17         V18         V19 
#> -1965.84311   923.95870    45.30015  1976.62242    58.04681   633.93594 
#>          V2         V20         V21         V22         V23         V24 
#>  -448.94781  -811.92042 -1438.30926  -446.14325   533.76321 -3600.95559 
#>         V25         V26         V27         V28         V29          V3 
#>  4691.39151  -701.57480 -1552.72546  1629.63646  -412.61476   777.61673 
#>         V30         V31         V32         V33         V34         V35 
#>  -294.99085   572.61023  -708.39364  -794.78207  1966.38381 -1802.32843 
#>         V36         V37         V38         V39          V4         V40 
#>  1373.19784  3169.37153 -1213.36662 -1595.59491 -3248.85121  1578.02919 
#>         V41         V42         V43         V44         V45         V46 
#> -1608.74882  1248.58616 -1831.67228 -2137.74375  -531.31338 -3161.98597 
#>         V47         V48         V49          V5         V50         V51 
#>   589.26086 -2785.01296 -1697.40005 -1424.51982   674.63525  -856.48427 
#>         V52         V53         V54         V55         V56         V57 
#>   -45.65690  -161.85493  -209.51030   540.77070   285.13956   199.97640 
#>         V58         V59          V6         V60          V7          V8 
#>  -201.92567  -591.57092   920.87997  -570.09044  1987.50407  4093.80125 
#>          V9 
#> -1361.00540 
#> 
#> Residual Deviance: 0.0001144722 
#> AIC: 122.0001 
#> classif.ce 
#>  0.2318841