Calculate feature importance

  1. Execution format
  2. Options

Execution format

catboost fstr [-m <model name>] [--input-path] <dataset> --fstr-type <output format>  [other parameters]

Options

Option Description Default value

--fstr-type

The feature importance output format.

Possible values:
 Required parameter

-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

--input-path

The name of the input file with the dataset description.

This parameter is required in the following cases:
  • The feature importance format is set to LossFunctionChange or ShapValues.
  • The feature impoertance format is set to PredictionValuesChange and the model does not contain information regarding the weight of leaves. All models trained with CatBoost version 0.9 or higher contain leaf weight information by default.
 input.tsv

--column-description

--cd

The path to the input file that contains the column descriptions.

This parameter is required in the following cases:
  • The feature importance format is set to LossFunctionChange or ShapValues.
  • The feature impoertance format is set to PredictionValuesChange and the model does not contain information regarding the weight of leaves. All models trained with CatBoost version 0.9 or higher contain leaf weight information by default.

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 name of the output file that contains the resulting values of the model for the input objects.

The format depends on the problem being solved.		 feature_strength.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
Option Description Default value

--fstr-type

The feature importance output format.

Possible values:
 Required parameter

-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

--input-path

The name of the input file with the dataset description.

This parameter is required in the following cases:
  • The feature importance format is set to LossFunctionChange or ShapValues.
  • The feature impoertance format is set to PredictionValuesChange and the model does not contain information regarding the weight of leaves. All models trained with CatBoost version 0.9 or higher contain leaf weight information by default.
 input.tsv

--column-description

--cd

The path to the input file that contains the column descriptions.

This parameter is required in the following cases:
  • The feature importance format is set to LossFunctionChange or ShapValues.
  • The feature impoertance format is set to PredictionValuesChange and the model does not contain information regarding the weight of leaves. All models trained with CatBoost version 0.9 or higher contain leaf weight information by default.

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 name of the output file that contains the resulting values of the model for the input objects.

The format depends on the problem being solved.		 feature_strength.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