AdaBoost: Demo of Adaboost using Sonar dataaset.
In jlin-vt/SML: Statistical Machine Learning
Description
Usage
Arguments
Value
See Also
Description
The base learner is based on Recursive Partitioning and Regression Trees.
Usage
1
AdaBoost(train, test, index.x, index.y, n.iter)
Arguments
train
training dataset.
test
test dataset.
index.x
index of X.
index.y
index of Y.
n.iter
the number of iterations.
Value
a list of objects:
train.error errors from training dataset.
test.error errors from test dataset.
See Also
http://web.stanford.edu/class/stats202/
jlin-vt/SML documentation built on Dec. 5, 2019, 2:05 a.m.
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Description Usage Arguments Value See Also
Description
The base learner is based on Recursive Partitioning and Regression Trees.
Usage
1 | AdaBoost(train, test, index.x, index.y, n.iter)
|
Arguments
train |
training dataset. |
test |
test dataset. |
index.x |
index of X. |
index.y |
index of Y. |
n.iter |
the number of iterations. |
Value
a list of objects: train.error errors from training dataset. test.error errors from test dataset.
See Also
http://web.stanford.edu/class/stats202/
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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