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What is iterations in CatBoost?
By default, CatBoost builds 1000 trees. The number of iterations can be decreased to speed up the training. When the number of iterations decreases, the learning rate needs to be increased. By default, the value of the learning rate is defined automatically depending on the number of iterations and the input dataset.
How do I tune a CatBoost model?
Steps taken in hyperparameter tuning
- Define a model.
- Define the range of possible values for all hyperparameters.
- Define a method for sampling hyperparameter values.
- Define evaluative criteria to judge the model.
- Define a cross-validation method.
How do you save a CatBoost model?
By default model is saved into model.py file, one could alter the output name using -m key. If there is more that one model-format specified, then the . py extention will be added to the name provided after -m key.
How does catboost work when overfitting occurs?
If overfitting occurs, CatBoost can stop the training earlier than the training parameters dictate. For example, it can be stopped before the specified number of trees are built.
How is training is performed-catboost.documentation?
How training is performed – CatBoost. Documentation The goal of training is to select the model , depending on a set of features , that best solves the given problem (regression, classification, or multiclassification) for any input object.
How to tune the number of iterations in catboost?
This can be done by setting the number of iterations to a large value, using the overfitting detector parameters and turning the use best model options on. In this case the resulting model contains only the first k best iterations, where k is the iteration with the best loss value on the validation dataset.
When to ignore the overfitting detector-catboost?
The number of iterations to continue the training after the iteration with the optimal metric value. IncToDec — Ignore the overfitting detector when the threshold is reached and continue learning for the specified number of iterations after the iteration with the optimal metric value.