xgbstack: Fit a stacking model given a measure of performance for each...

In reichlab/xgbstack: Flexible model stacking via gradient boosting for models that output predictive distributions.

Description

Fit a stacking model given a measure of performance for each component model on a set of training data, and a set of covariates to use in forming component model weights

An implementation of model stacking using xgboost.

Usage

xgbstack(formula, data, booster = "gbtree", subsample = 1,
  colsample_bytree = 1, colsample_bylevel = 1, max_depth = 6,
  min_child_weight = -10^10, eta = 0.3, gamma = 0, lambda = 0,
  alpha = 0, nrounds = 10, cv_params = NULL, cv_folds = NULL,
  cv_nfolds = 10L, cv_refit = "ttest", update = NULL, nthread = NULL,
  verbose = 0)

Arguments

formula	a formula describing the model fit. left hand side should give columns in data with scores of models, separated by +. right hand side should specify explanatory variables on which weights will depend.
data	a data frame with variables in formula
booster	what form of boosting to use? see xgboost documentation
subsample	fraction of data to use in bagging. not supported yet.
colsample_bytree	fraction of explanatory variables to randomly select in growing each regression tree. see xgboost documentation
colsample_bylevel	fraction of explanatory variables to randomly select in growing each level of the regression tree. see xgboost documentation
max_depth	maximum depth of regression trees. see xgboost documentation
min_child_weight	not recommended for use. see xgboost documentation
eta	learning rate. see xgboost documentation
gamma	Penalty on number of regression tree leafs. see xgboost documentation
lambda	L2 regularization of contribution to model weights in each round. see xgboost documentation
alpha	L1 regularization of contribution to model weights in each round. see xgboost documentation
nrounds	see xgboost documentation
cv_params	optional named list of parameter values to evaluate loss via cross-validation. Each component is a vector of parameter values with name one of "booster", "subsample", "colsample_bytree", "colsample_bylevel", "max_depth", "min_child_weight", "eta", "gamma", "lambda", "alpha", "nrounds"
cv_folds	list specifying observation groups to use in cross-validation each list component is a numeric vector of observation indices.
cv_nfolds	integer specifying the number of cross-validation folds to use. if cv_folds was provided, cv_nfolds is ignored. if cv_folds was not provided, the data will be randomly partitioned into cv_nfolds groups
cv_refit	character describing which of the models specified by the values in cv_params to refit using the full data set. Either "best", "ttest", or "none".
update	an object of class xgbstack to update
nthread	number of threads to use
verbose	how much output to generate along the way. 0 for no logging, 1 for some logging

Value

an estimated xgbstack object, which contains a gradient tree boosted fit mapping observed variables to component model weights

Author(s)

Evan Ray <[e-mail]>

reichlab/xgbstack documentation built on May 27, 2019, 4:54 a.m.