knngow: K-nn classification using Gower distance
In dprep: Data Pre-Processing and Visualization Functions for Classification
Description
Usage
Arguments
Value
Author(s)
See Also
Examples
Description
This function performs classification using the k- nearest neighbors but
using Gower distance rather than Euclidean distance. It is recommended
if the dataset has niminal and continuous attributes.
Usage
1
knngow(train, test, k)
Arguments
train
A matrix containing the training dataset.
test
A matrix containing the test dataset.
k
The number of neighbors to be used.
Value
predclass
A vector of predited classes
Author(s)
Edgar Acuna
See Also
Examples
1
2
3
dprep documentation built on May 29, 2017, 11:01 a.m.
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Description Usage Arguments Value Author(s) See Also Examples
Description
This function performs classification using the k- nearest neighbors but using Gower distance rather than Euclidean distance. It is recommended if the dataset has niminal and continuous attributes.
Usage
1 | knngow(train, test, k)
|
Arguments
train |
A matrix containing the training dataset. |
test |
A matrix containing the test dataset. |
k |
The number of neighbors to be used. |
Value
predclass |
A vector of predited classes |
Author(s)
Edgar Acuna
See Also
Examples
1 2 3 |
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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