## All functions

BaselearnerCategoricalBinary
Base learner to encode one single class of a categorical feature
BaselearnerCategoricalRidge
One-hot encoded base learner for a categorical feature
BaselearnerCentered
Centering a base learner by another one
BaselearnerCustom
Custom base learner using R functions.
BaselearnerPSpline
Non-parametric B or P-spline base learner
BaselearnerPolynomial
Polynomial base learner
BaselearnerTensor
Row-wise tensor product base learner
BlearnerFactoryList
Base learner factory list to define the set of base learners
CategoricalDataRaw
Data class for categorical variables
Compboost
Component-wise boosting
Compboost_internal
Internal Compboost Class This class is the raw C++ pendant and still at a very high-level. It is the base for the Compboost R6 class and provides many convenient wrapper to access data and execute methods by calling the C++ methods.
InMemoryData
Store data in RAM
LearnerClassifCompboost
LearnerCompboost
LearnerRegrCompboost
LoggerInbagRisk
Log the train risk.
LoggerIteration
Logger class to log the current iteration
LoggerList
Collect loggers
LoggerOobRisk
Log the validation/test/out-of-bag risk
LoggerTime
Log the runtime
LossAbsolute
LossBinomial
0-1 Loss for binary classification derived of the binomial distribution
LossCustom
Create LossCustom by using R functions.
LossHuber
LossQuadratic
LossQuantile
OptimizerAGBM
Nesterovs momentum
OptimizerCoordinateDescent
Coordinate descent
OptimizerCoordinateDescentLineSearch
Coordinate descent with line search
OptimizerCosineAnnealing
Coordinate descent with cosine annealing
ResponseBinaryClassif
Create response object for binary classification.
ResponseRegr
Create response object for regression.
boostComponents()
Wrapper to boost general additive models using components
boostLinear()
Wrapper to boost linear models for each feature.
boostSplines()
Wrapper to boost general additive models for each feature.
getCustomCppExample()
Get C++ example script to define a custom cpp logger
plotBaselearner()
Visualize contribution of one base learner
plotBaselearnerTraces()
Visualize base learner traces
plotFeatureImportance()
Visualize the feature importance
plotIndividualContribution()
Decompose the predicted value based on the given features
plotPEUni()
Visualize partial effect of a feature
plotRisk()
Visualize the risk
plotTensor()
Visualize bivariate tensor products