setAdaBoost {PatientLevelPrediction} | R Documentation |
Create setting for AdaBoost with python DecisionTreeClassifier base estimator
Description
Create setting for AdaBoost with python DecisionTreeClassifier base estimator
Usage
setAdaBoost(
nEstimators = list(10, 50, 200),
learningRate = list(1, 0.5, 0.1),
algorithm = list("SAMME"),
seed = sample(1e+06, 1)
)
Arguments
nEstimators |
(list) The maximum number of estimators at which boosting is terminated. In case of perfect fit, the learning procedure is stopped early. |
learningRate |
(list) Weight applied to each classifier at each boosting iteration. A higher learning rate increases the contribution of each classifier. There is a trade-off between the learningRate and nEstimators parameters There is a trade-off between learningRate and nEstimators. |
algorithm |
Only ‘SAMME’ can be provided. The 'algorithm' argument will be deprecated in scikit-learn 1.8. |
seed |
A seed for the model |
Value
a modelSettings object
Examples
## Not run:
model <- setAdaBoost(nEstimators = list(10),
learningRate = list(0.1),
seed = 42)
## End(Not run)
[Package PatientLevelPrediction version 6.4.1 Index]