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mlr 2.19.1.9000

Basics
Task Learner Train Predict
Preprocessing Tuning Resampling Benchmarking
Parallelization Performance Visualization
Use case - Regression
Advanced
mlr Configuration Wrapped Learners
Imputation Generic Bagging Advanced Tuning Feature Selection/Filtering Nested Resampling Imbalanced Classification Problems ROC Analysis and Performance Curves Learning Curve Analysis Partial Dependence Plots Classifier Calibration Hyperparameter Tuning Effects Out-of-Bag Predictions
Multilabel Classification Cost-Sensitive Classification Spatial Data Functional Data
Extending
Create Custom Learners Create Custom Measures Create Custom Imputation Methods Create Custom Filters
Appendix
Integrated Tasks Integrated Learners Integrated Measures Integrated Filter Methods
mlr Publications Talks, Videos and Workshops
mlrMBO mlrCPO mlrHyperopt OpenML
Changelog
Reference

Learner for kernel classification for functional data.

Source: R/RLearner_classif_fdausc.kernel.R

makeRLearner.classif.fdausc.kernel.Rd

Learner for kernel Classification.

Usage

# S3 method for classif.fdausc.kernel
makeRLearner()

Developed by Bernd Bischl, Michel Lang, Lars Kotthoff, Patrick Schratz, Julia Schiffner, Jakob Richter, Zachary Jones, Giuseppe Casalicchio, Mason Gallo.

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