Description Usage Arguments Value Author(s) Examples
The CVST methods needs a structured interface to both regression and classification data sets. These helper methods allow the construction and consistence handling of these types of data sets.
1 2 3 4 5 6 7 | constructData(x, y)
getN(data)
getSubset(data, subset)
getX(data, subset = NULL)
shuffleData(data)
isClassification(data)
isRegression(data)
|
x |
The feature data as vector or matrix. |
y |
The observed values (regressands/labels) as list, vector or factor. |
data |
A |
subset |
A index set. |
constructData
returns a CVST.data
object. getN
returns the number of data points in the data set. getSubset
returns a subset of the data as a CVST.data
object, while
getX
just return the feature data. shuffleData
returns a
randomly shuffled instance of the data.
Tammo Krueger <tammokrueger@googlemail.com>
1 2 3 4 5 6 7 8 | nsine = noisySine(10)
isClassification(nsine)
isRegression(nsine)
getN(nsine)
getX(nsine)
nsineShuffeled = shuffleData(nsine)
getX(nsineShuffeled)
getSubset(nsineShuffeled, 1:3)
|
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