Staged prediction
CatBoost allows to apply a trained model and calculate the results for each i-th tree of the model taking into consideration only the trees in the range [0; i).
Choose the implementation for more details.
Python package
Classes
CatBoost
Class purpose
Training and applying models.
Method
Description
Apply the model to the given dataset and calculate the results taking into consideration only the trees in the range [0; i).
CatBoostRegressor
Class purpose
Training and applying models for the regression problems. When using the applying methods only the predicted class is returned. Provides compatibility with the scikit-learn tools.
Method
Description
Apply the model to the given dataset and calculate the results taking into consideration only the trees in the range [0; i).
CatBoostClassifier
Class purpose
Training and applying models for the classification problems. Provides compatibility with the scikit-learn tools.
Methods
Description
Apply the model to the given dataset and calculate the results taking into consideration only the trees in the range [0; i).
Class purpose
The same as staged_predict with the difference that the results are probabilities that the object belongs to the positive class.
R package
For the catboost.staged_predict method:
Purpose
Apply the model to the given dataset and calculate the results for the specified trees only.
Command-line version
For the catboost calc command:
Purpose
Apply the model.
Command keys
--eval-period
Key description
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 eval-period.
This parameter defines the step to iterate over the range [--ntree-start; --ntree-end). For example, let's assume that the following parameter values are set:
--ntree-startis set 0--ntree-endis set to N (the total tree count)--eval-periodis set to 2
In this case, the results are returned for the following tree ranges: [0, 2), [0, 4), ... , [0, N).