Apply a model
Execution format
catboost calc [optional parameters]
Options
Option | Description | Default value |
---|---|---|
-m --model-file --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 |
--input-path | The name of the input file with the dataset description. | input.tsv |
--column-description --cd | 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. |
--input-pairs | The path to the input file that contains the pairs description for the dataset. This information is used for the calculation of Pairwise metrics. | Omitted Pairwise metrics require pairs of data. If this data is not provided explicitly by specifying this parameter, pairs are generated automatically in each group using object label values |
-o --output-path | Defines the output settings for the resulting values of the model. Supported value formats and types: The output data format depends on the machine learning task being solved.
| output.tsv |
--output-columns | A comma-separated list of columns names to output when forming the results of applying the model (including the ones obtained for the validation dataset when training). Prediction and feature values can be output for each object of the input dataset. Additionally, some column types can be output if specified in the input data. The output columns can be set in any order. Format:
Note. At least one of the specified columns must contain prediction values. For example, the following value raises an error:
| All columns that are supposed to be output according to the chosen parameters are output |
-T --thread-count | The number of threads to use during the training. Optimizes the speed of execution. This parameter doesn't affect results. | The number of processor cores |
--tree-count-limit | The number of trees from the model to use when applying. If specified, the first <value> trees are used. | 0 (if value equals to 0 this parameter is ignored and all trees from the model are used) |
--eval-period | To reduce the number of trees to use when the model is applied or the metrics are calculated, set the step of the trees to use to This parameter defines the step to iterate over the range
In this case, the results are returned for the following tree ranges: | 0 (the staged prediction mode is turned off) |
--prediction-type | A comma-separated list of prediction types. Supported prediction types:
| RawFormulaVal |
Option | Description | Default value |
---|---|---|
-m --model-file --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 |
--input-path | The name of the input file with the dataset description. | input.tsv |
--column-description --cd | 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. |
--input-pairs | The path to the input file that contains the pairs description for the dataset. This information is used for the calculation of Pairwise metrics. | Omitted Pairwise metrics require pairs of data. If this data is not provided explicitly by specifying this parameter, pairs are generated automatically in each group using object label values |
-o --output-path | Defines the output settings for the resulting values of the model. Supported value formats and types: The output data format depends on the machine learning task being solved.
| output.tsv |
--output-columns | A comma-separated list of columns names to output when forming the results of applying the model (including the ones obtained for the validation dataset when training). Prediction and feature values can be output for each object of the input dataset. Additionally, some column types can be output if specified in the input data. The output columns can be set in any order. Format:
Note. At least one of the specified columns must contain prediction values. For example, the following value raises an error:
| All columns that are supposed to be output according to the chosen parameters are output |
-T --thread-count | The number of threads to use during the training. Optimizes the speed of execution. This parameter doesn't affect results. | The number of processor cores |
--tree-count-limit | The number of trees from the model to use when applying. If specified, the first <value> trees are used. | 0 (if value equals to 0 this parameter is ignored and all trees from the model are used) |
--eval-period | To reduce the number of trees to use when the model is applied or the metrics are calculated, set the step of the trees to use to This parameter defines the step to iterate over the range
In this case, the results are returned for the following tree ranges: | 0 (the staged prediction mode is turned off) |
--prediction-type | A comma-separated list of prediction types. Supported prediction types:
| RawFormulaVal |