Figure 4

(a) Hold-out method. We employed a hold-out method for machine learning that splits data into the following two groups: a training dataset and a testing dataset. (b) Receiver operating characteristic curve of the classifier. (c) Characteristic distribution of the waveform. The pulse data are acquired 250,000 times per min. ‘Steps’ denote the number of data points acquired, ‘Height’ denotes the current value of the pulse, and ‘Peak ratio’ denotes the location of the peak of the pulse, when the left edge of the pulse is 0 and the right edge of the pulse is 1. (d) Zeta potential distribution of the bacteria. The dashed line denotes the median, and the dotted line denotes the quartiles. No statistically significant difference was noted between the two species.