Create setting for AdaBoost with python DecisionTreeClassifier base estimator

setAdaBoost(
  nEstimators = list(10, 50, 200),
  learningRate = list(1, 0.5, 0.1),
  algorithm = list("SAMME.R"),
  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: (list) If ‘SAMME.R’ then use the SAMME.R real boosting algorithm. base_estimator must support calculation of class probabilities. If ‘SAMME’ then use the SAMME discrete boosting algorithm. The SAMME.R algorithm typically converges faster than SAMME, achieving a lower test error with fewer boosting iterations.
seed: A seed for the model

Examples

if (FALSE) {
model.adaBoost <- setAdaBoost(nEstimators = list(10,50,200), learningRate = list(1, 0.5, 0.1),
                              algorithm = list('SAMME.R'), seed = sample(1000000,1)
                              )
}