Bayesian sampling is based on the Bayesian optimization algorithm and makes intelligent choices on the hyperparameter values to sample next. It picks the sample based on how the previous samples performed, such that the new sample improves the reported primary metric.




A named list containing each parameter and its distribution, e.g. list("parameter" = distribution).


The BayesianParameterSampling object.


When you use Bayesian sampling, the number of concurrent runs has an impact on the effectiveness of the tuning process. Typically, a smaller number of concurrent runs can lead to better sampling convergence, since the smaller degree of parallelism increases the number of runs that benefit from previously completed runs.

Bayesian sampling only supports choice(), uniform(), and quniform() distributions over the search space.

Bayesian sampling does not support any early termination policy. When using Bayesian parameter sampling, early_termination_policy must be NULL.

See also

choice(), uniform(), quniform()


if (FALSE) { param_sampling <- bayesian_parameter_sampling(list("learning_rate" = uniform(0.05, 0.1), "batch_size" = choice(c(16, 32, 64, 128)))) }