AdaBoost on Tree

Learning

training set file
testing set file
validation set file
output of the trained model file
Yes No self-consistency evaluation over training set
seed single random number generator
Yes No strict checking of dataset aganist algorithm

Decision Tree

Yes No estimate probabilities at leaf nodes
a confidence value to exceed to split child node
depth to stop splitting
minimum objs to split node
Yes No resuse the attributes durning growth
impurity criterion to choose best split
use a random attrute subset of size n

AdaBoost

Yes No force confidence rated boosting for probabilistic classifiers
number of boosting iterations

Selection

number of folds in n-fold cross-validation
the number of times to repeat the validation
the type of validation
Yes No use stratifed validation
Yes No force instance level validation for bagged set
Yes No force instances in positive bag to be positive
increment for progressive validation
validation model file
metric used to select best parameters
Yes No evaluate on the instance level
what to print while tuning

Validation

number of folds in n-fold cross-validation
the number of times to repeat the validation
the type of validation
Yes No use stratifed validation
Yes No force instance level validation for bagged set
Yes No force instances in positive bag to be positive
increment for progressive validation
validation model file

Format

index of class in feature vector
number of labels prefixing the feature
Dataset has header

Transform

Yes No use the product to expand the attribute space
list of classes to set positive, all else negative
name of positive class
name of negative class
begining of bag substring
end of bag substring