loss
(Package: bst) :
Internal Function
Internal Function
● Data Source:
CranContrib
● Keywords:
● Alias: balanced.folds, cvfolds, error.bars, gaussloss, gaussngra, hingeloss, hingengra, loss, loss.mbst, loss.mhingebst, mbst_fit, mhingebst_fit, ngradient, permute.rows, plotCVbst
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0 images
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cv.rmbst
(Package: bst) :
Cross-Validation for Truncated Multi-class Loss Boosting
Cross-validated estimation of the empirical multi-class loss for boosting parameter selection.
● Data Source:
CranContrib
● Keywords:
● Alias: cv.rmbst
●
0 images
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cv.mhingeova
(Package: bst) :
Cross-Validation for one-vs-all HingeBoost with multi-class problem
Cross-validated estimation of the empirical misclassification error for boosting parameter selection.
● Data Source:
CranContrib
● Keywords:
● Alias: cv.mhingeova
●
0 images
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cv.mada
(Package: bst) :
Cross-Validation for one-vs-all AdaBoost with multi-class problem
Cross-validated estimation of the empirical misclassification error for boosting parameter selection.
● Data Source:
CranContrib
● Keywords:
● Alias: cv.mada
●
0 images
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rbstpath
(Package: bst) :
Robust Boosting Path for Truncated Loss Functions
Gradient boosting path for optimizing robust loss functions with componentwise linear, smoothing splines, tree models as base learners.
● Data Source:
CranContrib
● Keywords: classification
● Alias: rbstpath
●
0 images
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bst_control
(Package: bst) :
Control Parameters for Boosting
Specification of the number of boosting iterations, step size and other parameters for boosting algorithms.
● Data Source:
CranContrib
● Keywords:
● Alias: bst_control
●
0 images
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bst.sel
(Package: bst) :
Function to select number of predictors
Function to determine the first q predictors in the boosting path, or perform (10-fold) cross-validation and determine the optimal set of parameters
● Data Source:
CranContrib
● Keywords: models, regression
● Alias: bst.sel
●
0 images
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bst
(Package: bst) :
Boosting for Classification and Regression
Gradient boosting for optimizing hinge or squared error loss functions with componentwise linear, smoothing splines, tree models as base learners.
● Data Source:
CranContrib
● Keywords: classification
● Alias: bst, coef.bst, fpartial.bst, plot.bst, predict.bst, print.bst
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0 images
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cv.rbst
(Package: bst) :
Cross-Validation for Truncated Loss Boosting
Cross-validated estimation of the empirical risk/error for truncated loss boosting parameter selection.
● Data Source:
CranContrib
● Keywords:
● Alias: cv.rbst
●
0 images
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cv.mbst
(Package: bst) :
Cross-Validation for Multi-class Boosting
Cross-validated estimation of the empirical multi-class loss for boosting parameter selection.
● Data Source:
CranContrib
● Keywords:
● Alias: cv.mbst
●
0 images
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providing 
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