fixVarNames()
|
Fixes variable names for data visualization purpose |
getAllBMRS()
|
Get all the benchmark result in a directory of directories |
getBMRTuningEntropy()
|
Compute the tuning entropy |
getBMR_perf_tune()
|
Selecting elements from BMR_res and cosmetic changes |
getBestBMRTune()
|
Get the tuning results of the optimal models |
getBestBMRTuningEntropy()
|
Get the tuning entropy corresponding to the optimal models |
getFreqBestFeatureSets()
|
Get the frequency of selection of a given feature across all regions |
getHyperparNames()
|
Get hyper-parameters names |
getTunePlot()
|
Plot the distribution of hyper-parameters resulting from the nested resampling |
get_BMR()
|
Retrieve benchmark results |
get_bestFeatureSets()
|
Get the optinmal feature sets |
makeAllFeatureImportancePlotFS()
|
Create feature importance across all regions of study |
makeAverageAUCPlot()
|
Make average AUC plot |
makeAverageAccPlot()
|
Make average accuracy plot |
makeBestTuneAUCPlot()
|
Make a violin plot comparing the results from the optimal models |
makeExampleModelSelectionPlot()
|
Make an example plot of model selection |
makeFeatureImportancePlot()
|
Makes a dot chart of feature importance |
makeTotalTimetrainPlot()
|
Make training time plot |
makeTuningEntropyPlot()
|
Create a plot of the evolution of tuning entropy with the number of selected features |
makeWindowInfluencePlot()
|
Visualize the influence of window size on model selection |
normH()
|
Calculates entropy |