Lrnr_multinom: sl3 extension: Feed-Forward Neural Networks and Multinomial...

In alexpkeil1/vibr: Variable Importance in Black-Box Regression

sl3 extension: Feed-Forward Neural Networks and Multinomial Log-Linear Models

Description

This learner provides feed-forward neural networks with a single hidden layer, and for multinomial log-linear models.

Format

R6Class object.

Value

Learner object with methods for both training and prediction. See Lrnr_base for documentation on learners.

Parameters

formula

A formula of the form class ~ x1 + x2 + ...

weights

(case) weights for each example – if missing defaults to 1

size

number of units in the hidden layer. Can be zero if there are skip-layer units.

entropy

switch for entropy (= maximum conditional likelihood) fitting. Default by least-squares.

decay

parameter for weight decay. Default 0.

maxit

maximum number of iterations. Default 100.

linout

switch for linear output units. Default logistic output units.

...

Other parameters passed to nnet.

Super class

sl3::Lrnr_base -> Lrnr_multinom

Methods

Public methods

• Lrnr_multinom$new()

• Lrnr_multinom$clone()

Inherited methods

• sl3::Lrnr_base$assert_trained()
• sl3::Lrnr_base$base_chain()
• sl3::Lrnr_base$base_predict()
• sl3::Lrnr_base$base_train()
• sl3::Lrnr_base$chain()
• sl3::Lrnr_base$custom_chain()
• sl3::Lrnr_base$get_outcome_range()
• sl3::Lrnr_base$get_outcome_type()
• sl3::Lrnr_base$predict()
• sl3::Lrnr_base$predict_fold()
• sl3::Lrnr_base$print()
• sl3::Lrnr_base$process_formula()
• sl3::Lrnr_base$reparameterize()
• sl3::Lrnr_base$retrain()
• sl3::Lrnr_base$sample()
• sl3::Lrnr_base$set_train()
• sl3::Lrnr_base$subset_covariates()
• sl3::Lrnr_base$train()
• sl3::Lrnr_base$train_sublearners()

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Method new()

Usage

Lrnr_multinom$new(decay = 0, maxit = 100, linout = FALSE, ...)

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Method clone()

The objects of this class are cloneable with this method.

Usage

Lrnr_multinom$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

Other Learners: Lrnr_density_gaussian, Lrnr_polspline_quiet, Lrnr_solnp_density_quiet, Lrnr_solnp_quiet, Lrnr_stepwise

alexpkeil1/vibr documentation built on Sept. 13, 2023, 3:20 a.m.