MATLAB File Help: prtFeatSelStatic |
prtFeatSelStatic Static feature selection object. FEATSEL = prtFeatSelStatic creates a static feature selection object. FEATSEL = prtFeatSelStatic('selectedFeatures', FEATURES) creates a static feature selection object with the selectedFeatures parameter set to FEATURES. A static feature selction object selects the features specified by the selectedFeatures parameter. Example: dataSet = prtDataGenIris; % Load a data set with 4 features StaticFeatSel = prtFeatSelStatic; % Create a static feature % selection object. StaticFeatSel.selectedFeatures = [1 3]; % Choose the first and % third feature % Training is not necessary for a static feature selection object, % the following command has no effect. StaticFeatSel = StaticFeatSel.train(dataSet); dataSetReduced = StaticFeatSel.run(dataSet); %Run the feature %selection explore(dataSetReduced);
Superclasses | prtFeatSel |
Sealed | false |
Construct on load | false |
prtFeatSelStatic | Static feature selection object. |
dataSet | The training prtDataSet, only stored if verboseStorage is true. |
dataSetSummary | Structure that summarizes prtDataSet. |
isCrossValidateValid | False |
isSupervised | True |
isTrained | Indicates if prtAction object has been trained. |
name | Static Feature Selection |
nameAbbreviation | StaticFeatSel |
selectedFeatures | The selected features |
showProgressBar | |
userData | User specified data |
verboseStorage | Specifies whether or not to store the training prtDataset. |
crossValidate | Cross validate prtAction using prtDataSet and cross validation keys. | |
get | get the object properties | |
kfolds | Perform K-folds cross-validation of prtAction | |
optimize | Optimize action parameter by exhaustive function maximization. | |
run | Run a prtAction object on a prtDataSet object. | |
set | set the object properties | |
train | Train a prtAction object using training a prtDataSet object. |