Model retraining using recipes

This page describes how to retrain a model using recipes.

To avoid any extra auto-generated individuals for refit or retrained experiments with a custom individual, set fixed_ensemble_level and fixed_num_individuals equal to the desired number of custom individuals in the final model. Also, ensure that enable_genetic_algorithm is set to off.

Additionally, you can set the following parameters to prevent the mutation of the genome for this individual (frozen case), in self.params dictionary in Individual Recipe’s set_params method:

prob_add_genes = 0.0
prob_prune_genes = 0.0
prob_prune_by_features = 0.0
prob_addbest_genes = 0.0
prob_prune_by_top_features = 0.0

Also, uncomment the following code in the Individual Recipe’s set_params method:


Note that the validation scores can have some randomness, but when the parent experiment has a reproducible level set with the relevant reproducible levels, the test scores should match.