BoostingOverfit#
- class BoostingOverfit[source]#
Check for overfit caused by using too many iterations in a gradient boosted model.
The check runs a pre-defined number of steps, and in each step it limits the boosting model to use up to X estimators (number of estimators is monotonic increasing). It plots the given score calculated for each step for both the train dataset and the test dataset.
- Parameters
- scorerUnion[Callable, str] , default: None
Scorer used to verify the model, either function or sklearn scorer name.
- scorer_namestr , default: None
Name to be displayed in the plot on y-axis. must be used together with ‘scorer’
- num_stepsint , default: 20
Number of splits of the model iterations to check.
- n_samplesint , default: 1_000_000
number of samples to use for this check.
- random_stateint, default: 42
random seed for all check internals.
- __init__(alternative_scorer: Optional[Tuple[str, Union[str, Callable]]] = None, num_steps: int = 20, n_samples: int = 1000000, random_state: int = 42, **kwargs)[source]#
- __new__(*args, **kwargs)#
Methods
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Add new condition function to the check. |
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Add condition. |
Remove all conditions from this check instance. |
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Run conditions on given result. |
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Return check instance config. |
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Return check object from a CheckConfig object. |
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Deserialize check instance from JSON string. |
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Return check metadata. |
Name of class in split camel case. |
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Return parameters to show when printing the check. |
Remove given condition by index. |
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Run check. |
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Run check. |
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Serialize check instance to JSON string. |
