Function reference • compboost

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

InMemoryData: Store data in RAM

LearnerClassifCompboost: Component-wise gradient boosting classification learner

LearnerCompboost: Component-wise gradient boosting learner

LearnerRegrCompboost: Component-wise gradient boosting regression learner

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: Absolute loss for regression tasks.

LossBinomial: 0-1 Loss for binary classification derived of the binomial distribution

LossCustom: Create LossCustom by using R functions.

LossHuber: Huber loss for regression tasks.

LossQuadratic: Quadratic loss for regression tasks.

LossQuantile: Quantile loss for regression tasks.

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