Calculate object importance

Purpose

Calculate the effect of objects from the training dataset on the optimized metric values for the objects from the validation dataset:

Positive values reflect that the optimized metric increases.
Negative values reflect that the optimized metric decreases.

The higher the deviation from 0, the bigger the impact that an object has on the optimized metric.

This mode is an implementation of the approach described in the Finding Influential Training Samples for Gradient Boosted Decision Trees paper .

Execution format

catboost ostr [optional parameters]

Options


Option	Description	Default value
-m --model-path	The name of the input file with the description of the model obtained as the result of training.	model.bin
--model-format	The format of the input model. Possible values: CatboostBinary. AppleCoreML (only datasets without categorical features are currently supported). json (multiclassification models are not currently supported). Refer to the CatBoost JSON model tutorial for format details.	CatboostBinary
-f --learn-set	The path to the input file that contains the dataset description. Format: `[scheme://]<path>` `scheme` (optional) defines the type of the input dataset. Possible values: `quantized://` — catboost.Pool quantized pool. If omitted, a dataset in Native CatBoost format is expected. `path` defines the path to the dataset description.	Required parameter (the path must be specified).
-t --test-set	The path to the input file that contains the validation dataset description (the format must be the same as used in the training dataset).	Required parameter
--cd --column-description	The path to the input file that contains the columns description.	If omitted, it is assumed that the first column in the file with the dataset description defines the label value, and the other columns are the values of numerical features.
-o --output-path	The path to the output file with calculated object importances.	object_importances.tsv
-T --thread-count	The number of threads to use during training. Optimizes the speed of execution. This parameter doesn't affect results.	The number of processor cores
--delimiter	The delimiter character used to separate the data in the dataset description input file. Only single char delimiters are supported. If the specified value contains more than one character, only the first one is used.	The input data is assumed to be tab-separated
--has-header	Read the column names from the first line if this parameter is set to `True`.	False
--update-method	The algorithm accuracy method. Possible values: SinglePoint — The fastest and least accurate method. TopKLeaves — Specify the number of leaves. The higher the value, the more accurate and the slower the calculation. AllPoints — The slowest and most accurate method. Supported parameters: `top` — Defines the number of leaves to use for the 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 TopKLeaves and limits the number of leaves to 3: `TopKLeaves:top=3`	SinglePoint


Option	Description	Default value
-m --model-path	The name of the input file with the description of the model obtained as the result of training.	model.bin
--model-format	The format of the input model. Possible values: CatboostBinary. AppleCoreML (only datasets without categorical features are currently supported). json (multiclassification models are not currently supported). Refer to the CatBoost JSON model tutorial for format details.	CatboostBinary
-f --learn-set	The path to the input file that contains the dataset description. Format: `[scheme://]<path>` `scheme` (optional) defines the type of the input dataset. Possible values: `quantized://` — catboost.Pool quantized pool. If omitted, a dataset in Native CatBoost format is expected. `path` defines the path to the dataset description.	Required parameter (the path must be specified).
-t --test-set	The path to the input file that contains the validation dataset description (the format must be the same as used in the training dataset).	Required parameter
--cd --column-description	The path to the input file that contains the columns description.	If omitted, it is assumed that the first column in the file with the dataset description defines the label value, and the other columns are the values of numerical features.
-o --output-path	The path to the output file with calculated object importances.	object_importances.tsv
-T --thread-count	The number of threads to use during training. Optimizes the speed of execution. This parameter doesn't affect results.	The number of processor cores
--delimiter	The delimiter character used to separate the data in the dataset description input file. Only single char delimiters are supported. If the specified value contains more than one character, only the first one is used.	The input data is assumed to be tab-separated
--has-header	Read the column names from the first line if this parameter is set to `True`.	False
--update-method	The algorithm accuracy method. Possible values: SinglePoint — The fastest and least accurate method. TopKLeaves — Specify the number of leaves. The higher the value, the more accurate and the slower the calculation. AllPoints — The slowest and most accurate method. Supported parameters: `top` — Defines the number of leaves to use for the 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 TopKLeaves and limits the number of leaves to 3: `TopKLeaves:top=3`	SinglePoint