SensitivityToNorm: Computes sensitive to normalization statistics.
In sevvandi/outselect: Algorithm selection for unsupervised outlier detection
Description
Usage
Arguments
Value
Examples
Description
Computes sensitive to normalization statistics.
Usage
1
SensitivityToNorm(rocpr = 1)
Arguments
rocpr
If rocpr=1 then area under ROC curve is used as the performance measure. If rocpr=2 area under PR curve is used.
Value
A list containing the following:
- friedman
The output of the Friedman test.
- nemenyi
The output of the Nemenyi test tsutils::nemenyi.
- rangediff
For each outlier method and each dataset, the maximum performance minus the minimum performance.
Examples
1
2
out <- SensitivityToNorm(1)
out
sevvandi/outselect documentation built on June 1, 2019, 3:58 a.m.
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.
Description Usage Arguments Value Examples
Description
Computes sensitive to normalization statistics.
Usage
1 | SensitivityToNorm(rocpr = 1)
|
Arguments
rocpr |
If |
Value
A list containing the following:
- friedman
The output of the Friedman test.
- nemenyi
The output of the Nemenyi test
tsutils::nemenyi.- rangediff
For each outlier method and each dataset, the maximum performance minus the minimum performance.
Examples
1 2 | out <- SensitivityToNorm(1)
out
|
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.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.