LearnerClassifLiblineaR: LiblineaR Classification Learner

In zellerlab/siamcat: Statistical Inference of Associations between Microbial Communities And host phenoTypes

LiblineaR Classification Learner

Description

LiblineaR Classification Learner

LiblineaR Classification Learner

Details

Type of SVC depends on type argument:

• 0 L2-regularized logistic regression (primal)

• 1 L2-regularized L2-loss support vector classification (dual)

• 3 L2-regularized L1-loss support vector classification (dual)

• 2 L2-regularized L2-loss support vector classification (primal)

• 4 Support vector classification by Crammer and Singer

• 5 L1-regularized L2-loss support vector classification

• 6 L1-regularized logistic regression

• 7 L2-regularized logistic regression (dual)

If number of records > number of features, type = 2 is faster than type = 1 (Hsu et al. 2003).

Note that probabilistic predictions are only available for types 0, 6, and 7. The default epsilon value depends on the type parameter, see [LiblineaR::LiblineaR].

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifLiblineaR

Methods

Public methods

• LearnerClassifLiblineaR$new()

• LearnerClassifLiblineaR$clone()

Inherited methods

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

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Method new()

#' Creates a new instance of this [R6][R6::R6Class] class.

Usage

LearnerClassifLiblineaR$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifLiblineaR$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

zellerlab/siamcat documentation built on Feb. 1, 2024, 2:21 a.m.