| catboost.get_object_importance {catboost} | R Documentation |
Calculate the object importances (see https://catboost.ai/docs/concepts/ostr.html). This is the implementation of the LeafInfluence algorithm from the following paper: https://arxiv.org/pdf/1802.06640.pdf
catboost.get_object_importance( model, pool, train_pool, top_size = -1, type = "Average", update_method = "SinglePoint", thread_count = -1, ostr_type = NULL )
model |
The model obtained as the result of training. Default value: Required argument |
pool |
The pool for which you want to evaluate the object importances. Default value: Required argument |
train_pool |
The pool on which the model has been trained. Default value: Required argument |
top_size |
Method returns the result of the top_size most important train objects. If -1, then the top size is not limited. Default value: -1 |
type |
Possible values:
Default value: 'Average' |
update_method |
Description of the update set methods are given in section 3.1.3 of the paper. Possible values:
Default value: 'SinglePoint' |
thread_count |
The number of threads to use when applying the model. If -1, then the number of threads is set to the number of CPU cores. Allows you to optimize the speed of execution. This parameter doesn't affect results. Default value: -1 |
ostr_type |
Deprecated parameter, use 'type' instead. |
List with elements "indices" and "scores".
https://catboost.ai/docs/concepts/r-reference_catboost-get_object_importance.html