Fig. 1From: Automatic classification of the physical surface in sound uroflowmetry using machine learning methodsPredictive power (importance) of each frequency component in the classification task with three classes: ceramic, water and silence. The frequency band selected in our algorithms is shown in blue. The importance is calculated using the Gini impurity with a random forest modelBack to article page