Difference between revisions of "Classification analysis"
From BioUML platform
m (Protected "Classification analysis": Autogenerated page ([edit=sysop] (indefinite))) |
(Automatic synchronization with BioUML) |
||
| Line 15: | Line 15: | ||
* '''Classification mode''' – Select classification mode | * '''Classification mode''' – Select classification mode | ||
* '''Classification type''' – Select classification type | * '''Classification type''' – Select classification type | ||
| + | * '''Are covariance matrices equal?''' – Are covariance matrices (within classes/groups/clusters) equal? | ||
* '''Path to table with data matrix''' – Path to table with data matrix | * '''Path to table with data matrix''' – Path to table with data matrix | ||
* '''Variable names''' – Select variable names | * '''Variable names''' – Select variable names | ||
| − | * '''Classifier name''' – Select classifier name | + | * '''Classifier name''' – Select classifier name: classifier consists of names of classes (samples, groups, clusters) |
* '''Path to folder with saved model''' – Path to folder with saved model | * '''Path to folder with saved model''' – Path to folder with saved model | ||
* '''Percentage of data for training''' – Percentage of input data set (this part of input data set will be used as training set) | * '''Percentage of data for training''' – Percentage of input data set (this part of input data set will be used as training set) | ||
Revision as of 19:00, 13 February 2017
- Analysis title
Classification analysis
- Provider
- Institute of Systems Biology
- Class
ClassificationAnalysis- Plugin
- biouml.plugins.bindingregions (Binding-regions related analyses)
Description
Create and save classification model or load classification model for prediction of classes or cross-validation of classification model
Parameters:
- Classification mode – Select classification mode
- Classification type – Select classification type
- Are covariance matrices equal? – Are covariance matrices (within classes/groups/clusters) equal?
- Path to table with data matrix – Path to table with data matrix
- Variable names – Select variable names
- Classifier name – Select classifier name: classifier consists of names of classes (samples, groups, clusters)
- Path to folder with saved model – Path to folder with saved model
- Percentage of data for training – Percentage of input data set (this part of input data set will be used as training set)
- Path to output folder – Output folder will be created under this location when it doesn't exist
