Last data update: 2014.03.03
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R: Cross-Validation for one-vs-all AdaBoost with multi-class...
Cross-Validation for one-vs-all AdaBoost with multi-class problem
Description
Cross-validated estimation of the empirical misclassification error for boosting parameter selection.
Usage
cv.mada(x, y, balance=FALSE, K=10, nu=0.1, mstop=200, interaction.depth=1,
trace=FALSE, plot.it = TRUE, se = TRUE, ...)
Arguments
x |
a data matrix 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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nu |
a small number (between 0 and 1) defining the step size or shrinkage parameter.
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mstop |
number of boosting iteration.
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interaction.depth |
used in gbm to specify the depth of trees.
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trace |
if TRUE, iteration results printed out.
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plot.it |
a logical value, to plot the cross-validation error if TRUE .
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se |
a logical value, to plot with 1 standard deviation curves.
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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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...
See Also
mada
Results
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