Last data update: 2014.03.03
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R: Cross-Validation for one-vs-all HingeBoost with multi-class...
cv.mhingeova | R Documentation |
Cross-Validation for one-vs-all HingeBoost with multi-class problem
Description
Cross-validated estimation of the empirical misclassification error for boosting parameter selection.
Usage
cv.mhingeova(x, y, balance=FALSE, K=10, cost = NULL, nu=0.1,
learner=c("tree", "ls", "sm"), maxdepth=1, m1=200, twinboost = FALSE,
m2=200, trace=FALSE, plot.it = TRUE, se = TRUE, ...)
Arguments
x |
a data frame containing the variables in the model.
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y |
vector of multi class responses. y must be an interger vector from 1 to C for C class problem.
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balance |
logical value. If TRUE, The K
parts were roughly balanced, ensuring that the classes were distributed
proportionally among each of the K parts.
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K |
K-fold cross-validation
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cost |
price to pay for false positive, 0 < cost < 1; price of false negative is 1-cost .
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nu |
a small number (between 0 and 1) defining the step size or shrinkage parameter.
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learner |
a character specifying the component-wise base learner to be used:
ls linear models,
sm smoothing splines,
tree regression trees.
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maxdepth |
tree depth used in learner=tree
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m1 |
number of boosting iteration
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twinboost |
logical: twin boosting?
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m2 |
number of twin boosting iteration
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trace |
if TRUE, iteration results printed out
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plot.it |
a logical value, to plot the estimated risks if TRUE .
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se |
a logical value, to plot with standard errors.
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... |
additional arguments.
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Value
object with
residmat |
empirical risks in each cross-validation at boosting iterations
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fraction |
abscissa values at which CV curve should be computed.
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cv |
The CV curve at each value of fraction
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cv.error |
The standard error of the CV curve
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...
Note
The functions for balanced cross validation were from R package pmar.
See Also
mhingeova
Results
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