Function reference • mlr3mbo

All functions

AcqFunction: Acquisition Function Base Class

AcqOptimizer: Acquisition Function Optimizer

Surrogate: Surrogate Model

SurrogateLearner: Surrogate Model Containing a Single Learner

SurrogateLearnerCollection: Surrogate Model Containing Multiple Learners

acqf(): Syntactic Sugar Acquisition Function Construction

acqo(): Syntactic Sugar Acquisition Function Optimizer Construction

default_acqfun(): Default Acquisition Function

default_acqopt(): Default Acquisition Function Optimizer

default_loopfun(): Default Loop Function

default_surrogate(): Default Surrogate

loop_function: Loop Functions for Bayesian Optimization

mbo_defaults: Defaults for OptimizerMbo

mlr3mbo mlr3mbo-package: mlr3mbo: Flexible Bayesian Optimization

mlr_acqfunctions: Dictionary of Acquisition Functions

mlr_acqfunctions_cb AcqFunctionCB: Acquisition Function Confidence Bound

mlr_acqfunctions_ei AcqFunctionEI: Acquisition Function Expected Improvement

mlr_acqfunctions_eips AcqFunctionEIPS: Acquisition Function Expected Improvement Per Second

mlr_acqfunctions_mean AcqFunctionMean: Acquisition Function Mean

mlr_acqfunctions_pi AcqFunctionPI: Acquisition Function Probability of Improvement

mlr_acqfunctions_smsego AcqFunctionSmsEgo: Acquisition Function SMS-EGO

mlr_loop_functions: Dictionary of Loop Functions

bayesopt_ego(): Sequential Single-Objective Bayesian Optimization

bayesopt_mpcl(): Single-Objective Bayesian Optimization via Multipoint Constant Liar

bayesopt_parego(): Multi-Objective Bayesian Optimization via ParEGO

bayesopt_smsego(): Sequential Multi-Objective Bayesian Optimization via SMS-EGO

mlr_optimizers_mbo OptimizerMbo: Model Based Optimization

mlr_tuners_mbo TunerMbo: Tuner using Model Based Optimization

result_by_default(): Choose Final Point(s) based on the Archive

result_by_surrogate_design(): Choose Final Point(s) by Surrogate Mean

srlrn(): Syntactic Sugar Surrogate Learner Construction

srlrnc(): Syntactic Sugar Surrogate Learner Collection Construction