mlexperiments (version 0.0.5)

LearnerKnn: LearnerKnn R6 class

Description

This learner is a wrapper around class::knn() in order to perform a k-nearest neighbor classification.

Super class

mlexperiments::MLLearnerBase -> LearnerKnn

Methods

Public methods

• LearnerKnn$new()

• LearnerKnn$clone()

Inherited methods

• mlexperiments::MLLearnerBase$bayesian_scoring_function()
• mlexperiments::MLLearnerBase$cross_validation()
• mlexperiments::MLLearnerBase$fit()
• mlexperiments::MLLearnerBase$predict()

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Method new()

Create a new LearnerKnn object.

Usage

LearnerKnn$new()

Details

This learner is a wrapper around class::knn() in order to perform a k-nearest neighbor classification. The following experiments are implemented:

• MLTuneParameters

• MLCrossValidation

• MLNestedCV For the two hyperparameter optimization strategies ("grid" and "bayesian"), the parameter metric_optimization_higher_better of the learner is set to FALSE by default as the classification error rate (mlr3measures::ce()) is used as the optimization metric.

Examples

LearnerKnn$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerKnn$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Details

Optimization metric: classification error rate Can be used with

• MLTuneParameters

• MLCrossValidation

• MLNestedCV

Implemented methods:

• $fit To fit the model.

• $predict To predict new data with the model.

• $cross_validation To perform a grid search (hyperparameter optimization).

• $bayesian_scoring_function To perform a Bayesian hyperparameter optimization.

For the two hyperparameter optimization strategies ("grid" and "bayesian"), the parameter metric_optimization_higher_better of the learner is set to FALSE by default as the classification error rate (mlr3measures::ce()) is used as the optimization metric.

See Also

class::knn(), mlr3measures::ce()

class::knn(), mlr3measures::ce()

Examples

LearnerKnn$new()


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## Method `LearnerKnn$new`
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LearnerKnn$new()