setAdaBoost: Create setting for AdaBoost with python...

In OHDSI/PatientLevelPrediction: Develop Clinical Prediction Models Using the Common Data Model

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)

OHDSI/PatientLevelPrediction documentation built on Feb. 14, 2025, 9:44 a.m.