Calculate object importance
Purpose
- Positive values reflect that the optimized metric increases.
- Negative values reflect that the optimized metric decreases.
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 |
| -f --learn-set | The path to the input file that contains 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 column descriptions. | 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. | 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 | False |
| --update-method | The algorithm accuracy method. Possible values:
Supported parameters:
For example, the following value sets the method to TopKLeaves and limits the number of leaves to 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 |
| -f --learn-set | The path to the input file that contains 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 column descriptions. | 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. | 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 | False |
| --update-method | The algorithm accuracy method. Possible values:
Supported parameters:
For example, the following value sets the method to TopKLeaves and limits the number of leaves to 3: | SinglePoint |