Cross-validate a gbm
Functions for cross-validating gbm. These functions are used internally and are not intended for end-user direct usage.
gbmCrossVal( cv.folds, nTrain, n.cores, class.stratify.cv, data, x, y, offset, distribution, w, var.monotone, n.trees, interaction.depth, n.minobsinnode, shrinkage, bag.fraction, var.names, response.name, group ) gbmCrossValErr(cv.models, cv.folds, cv.group, nTrain, n.trees) gbmCrossValPredictions( cv.models, cv.folds, cv.group, best.iter.cv, distribution, data, y ) gbmCrossValModelBuild( cv.folds, cv.group, n.cores, i.train, x, y, offset, distribution, w, var.monotone, n.trees, interaction.depth, n.minobsinnode, shrinkage, bag.fraction, var.names, response.name, group ) gbmDoFold( X, i.train, x, y, offset, distribution, w, var.monotone, n.trees, interaction.depth, n.minobsinnode, shrinkage, bag.fraction, cv.group, var.names, response.name, group, s )
cv.folds |
The number of cross-validation folds. |
nTrain |
The number of training samples. |
n.cores |
The number of cores to use. |
class.stratify.cv |
Whether or not stratified cross-validation samples are used. |
data |
The data. |
x |
The model matrix. |
y |
The response variable. |
offset |
The offset. |
distribution |
The type of loss function. See |
w |
Observation weights. |
var.monotone |
See |
n.trees |
The number of trees to fit. |
interaction.depth |
The degree of allowed interactions. See
|
n.minobsinnode |
See |
shrinkage |
See |
bag.fraction |
See |
var.names |
See |
response.name |
See |
group |
Used when |
cv.models |
A list containing the models for each fold. |
cv.group |
A vector indicating the cross-validation fold for each member of the training set. |
best.iter.cv |
The iteration with lowest cross-validation error. |
i.train |
Items in the training set. |
X |
Index (cross-validation fold) on which to subset. |
s |
Random seed. |
These functions are not intended for end-user direct usage, but are used
internally by gbm
.
A list containing the cross-validation error and predictions.
Greg Ridgeway gregridgeway@gmail.com
J.H. Friedman (2001). "Greedy Function Approximation: A Gradient Boosting Machine," Annals of Statistics 29(5):1189-1232.
L. Breiman (2001). https://www.stat.berkeley.edu/users/breiman/randomforest2001.pdf.
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