catboost.get_object_importance

catboost.get_object_importance(model,
                               pool,
                               train_pool,
                               top_size = -1,
                               type = '{{ fit__ostr__ostr_type__PerPool }}',
                               update_method = 'SinglePoint',
                               thread_count = -1)

{{ dl--purpose }} {#purpose}

{% include sections-with-methods-desc-get_object_importance__div %}

{{ dl--args }} {#arguments}

model

Description

The model obtained as the result of training.

Default value

{{ r--required }}

pool

Description

The input dataset.

{% if audience == "internal" %}

For datasets input as files

{% include files-internal-files-internal__desc__full %}

{% endif %}

Default value

{{ r--required }}

train_pool

Description

The dataset used for training.

Default value

{{ r--required }}

top_size

Description

Defines the number of most important objects from the training dataset. The number of returned objects is limited to this number.

Default value

{{ fit__ostr__top_size }}

type

Description

The method for calculating the object importances.

Possible values:

• {{ fit__ostr__ostr_type__PerPool }} — The average of scores of objects from the training dataset for every object from the input dataset.
• {{ fit__ostr__ostr_type__PerObject }} — The scores of each object from the training dataset for each object from the input dataset.

Default value

{{ fit__ostr__ostr_type }}

update_method

Description

The algorithm accuracy method.

Possible values:

• {{ ostr__update-method__SinglePoint }} — The fastest and least accurate method.
• {{ ostr__update-method__TopKLeaves }} — Specify the number of leaves. The higher the value, the more accurate and the slower the calculation.
• {{ ostr__update-method__AllPoints }} — The slowest and most accurate method.

Supported parameters:

• top — Defines the number of leaves to use for the {{ ostr__update-method__TopKLeaves }} update method. See the Finding Influential Training Samples for Gradient Boosted Decision Trees for more details.

For example, the following value sets the method to {{ ostr__update-method__TopKLeaves }} and limits the number of leaves to 3:

TopKLeaves:top=3

Default value

{{ ostr__update-method__default }}

thread_count

Description

{% include reusage-thread-count-short-desc %}

{% include reusage-thread_count__cpu_cores__optimizes-the-speed-of-execution %}

Default value

{{ fit__thread_count__wrappers }}

{{ dl--example }} {#example}

{% include ostr__r-object-strength__r__p %}