Hyperparameter Optimization for 'mlr3'
Rush Data Storage
Frozen Rush Data Storage
Class for Logging Evaluated Hyperparameter Configurations
Convert to a Search Space
Convert to a Tuner
Assertions for Callbacks
Assertions for Callbacks
Function for Automatic Tuning
Class for Automatic Tuning
Create Asynchronous Tuning Callback
Create Batch Tuning Callback
Asynchronous Tuning Callback
Create Batch Tuning Callback
Asynchronous Tuning Context
Batch Tuning Context
Extract Inner Tuning Archives
Extract Inner Tuning Results
Hyperparameter Tuning with Asynchronous Design Points
Hyperparameter Tuning with Asynchronous Grid Search
Hyperparameter Tuning with Asynchronous Random Search
Hyperparameter Tuning with Covariance Matrix Adaptation Evolution Stra...
Hyperparameter Tuning with Design Points
Hyperparameter Tuning with Generalized Simulated Annealing
Hyperparameter Tuning with Grid Search
Hyperparameter Tuning with Internal Tuning
Hyperparameter Tuning with Iterated Racing.
Hyperparameter Tuning with Non-linear Optimization
Hyperparameter Tuning with Random Search
Dictionary of Tuners
Assertion for mlr3tuning objects
mlr3tuning: Hyperparameter Optimization for 'mlr3'
MLflow Connector Callback
Default Configuration Callback
Freeze Archive Callback
Save Logs Callback
Backup Benchmark Result Callback
Measure Callback
One Standard Error Rule Callback
Class for Tuning Objective
Class for Tuning Objective
Class for Tuning Objective
Objects exported from other packages
Configure Validation for AutoTuner
Syntactic Sugar for Asynchronous Tuning Instance Construction
Syntactic Sugar for Tuning Instance Construction
Syntactic Sugar for Tuning Objects Construction
Function for Nested Resampling
Function for Tuning a Learner
Tuner
Class for Asynchronous Tuning Algorithms
TunerAsyncFromOptimizerAsync
Class for Batch Tuning Algorithms
TunerBatchFromOptimizerBatch
Multi-Criteria Tuning with Rush
Single Criterion Tuning with Rush
Class for Multi Criteria Tuning
Class for Single Criterion Tuning
Multi Criteria Tuning Instance for Batch Tuning
Single Criterion Tuning Instance for Batch Tuning
Hyperparameter optimization package of the 'mlr3' ecosystem. It features highly configurable search spaces via the 'paradox' package and finds optimal hyperparameter configurations for any 'mlr3' learner. 'mlr3tuning' works with several optimization algorithms e.g. Random Search, Iterated Racing, Bayesian Optimization (in 'mlr3mbo') and Hyperband (in 'mlr3hyperband'). Moreover, it can automatically optimize learners and estimate the performance of optimized models with nested resampling.
Useful links