Meta parameters are used to control the search space and the model configuration. In BSR, they are mainly defined in the theorist constructor (see
regressor.py). Below is a basic overview of these parameters. Note, there are additional algorithm-irrelevant configurations that can be customized in the constructor; please refer to code documentation for their details.
tree_num: the number of expression trees to use in the linear mixture (final prediction model); also denoted by
iter_num: the number of RJ-MCMC steps to execute (note: this can also be understood as the number of
K-samples to take in the fitting process).
val: the number of validation steps to execute following each iteration.
beta: the hyperparameter that controls growth of a new expression tree. This needs to be < 0, and in general, smaller values of
betacorrespond to deeper expression trees.