Cross-validation
CatBoost allows to perform cross-validation on the given dataset.
Choose the implementation for more details.
Command | Purpose | Command keys | Key description |
---|---|---|---|
catboost fit | Training can be launched in cross-validation mode. In this case, only the training dataset is required. This dataset is split, and the resulting folds are used as the learning and evaluation datasets. If the input dataset contains the GroupId column, all objects from one group are added to the same fold. Each cross-validation run from the command-line interface launches one training out of N trainings in N-fold cross-validation. Use one of the following methods to get aggregated N-fold cross-validation results:
| --cv | Enable the cross-validation mode and specify the launching parameters. Format:
The following cross-validation types (cv_type) are supported:
|
--cv-rand | Use this as the seed value for random permutation of the data. The permutation is performed before splitting the data for cross-validation. Each seed generates unique data splits. It must be used with the --cv parameter type set to Classical or Inverted. |
Command | Purpose | Command keys | Key description |
---|---|---|---|
catboost fit | Training can be launched in cross-validation mode. In this case, only the training dataset is required. This dataset is split, and the resulting folds are used as the learning and evaluation datasets. If the input dataset contains the GroupId column, all objects from one group are added to the same fold. Each cross-validation run from the command-line interface launches one training out of N trainings in N-fold cross-validation. Use one of the following methods to get aggregated N-fold cross-validation results:
| --cv | Enable the cross-validation mode and specify the launching parameters. Format:
The following cross-validation types (cv_type) are supported:
|
--cv-rand | Use this as the seed value for random permutation of the data. The permutation is performed before splitting the data for cross-validation. Each seed generates unique data splits. It must be used with the --cv parameter type set to Classical or Inverted. |