R: Calculate the object importances

catboost.get_object_importance {catboost} | R Documentation

## Calculate the object importances

### Description

Calculate the object importances (see https://tech.yandex.com/catboost/doc/dg/concepts/about-docpage/). This is the implementation of the LeafInfluence algorithm from the following paper: https://arxiv.org/pdf/1802.06640.pdf

### Usage

    catboost.get_object_importance(model, pool, train_pool, top_size = -1,
      ostr_type = "Average", update_method = "SinglePoint",
      thread_count = -1)

### Arguments

`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 was 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
`ostr_type` | The ostr type. Possible values: 'Average' Method returns the mean train objects scores for all input objects. 'PerObject' Method returns the train objects scores for every input object. Default value: 'Average'
`update_method` | Description of the update set methods are given in section 3.1.3 of the paper. Possible values: 'SinglePoint' 'TopKLeaves' It is posible to set top size : TopKLeaves:top=2. 'AllPoints' 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 cores. Allows you to optimize the speed of execution. This parameter doesn't affect results. Default value: -1

### See Also

https://tech.yandex.com/catboost/doc/dg/concepts/

[Package catboost version 0.11.1 Index]