Description Usage Arguments Details Value Author(s) References See Also
Functions for cross-validating gbm. These functions are used internally and are not intended for end-user direct usage.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | 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, mFeatures
var.names, response.name, group)
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, mFeatures, 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, mFeatures,
cv.group, var.names, response.name, group, s)
gbmCrossValErr(cv.models, cv.folds, cv.group, nTrain, n.trees)
gbmCrossValPredictions(cv.models, cv.folds, cv.group,
best.iter.cv, distribution, data, y)
|
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 |
mFeatures |
See |
var.names |
See |
response.name |
See |
group |
Used when |
i.train |
Items in the training set. |
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. |
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). Random Forests.
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