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All methods performed worse than on the random split benchmark.
This random split was also re-performed prior to building every model.
This was done for both the random split set and temporal split set.
To accurately compare the methods, 4 z-scores were calculated for each method and metric within the experiments (random split MCC, random split BEDROC, temporal split MCC, and temporal split BEDROC, Table 1 and Fig. 4).
Models were trained on 70% of the random split set and then validated on the remaining 30%.
Firstly, the accuracy of all algorithms was estimated with help of the random split method (Fig. 2).
Similar(13)
We report the mean recognition accuracy over 20 random splits.
Using other preprocessing methods and multiple random splitting of the data sets obtained the similar results.
We also report the mean recognition accuracy over 20 random splits.
The average recognition accuracies are reported for 30 repeated random splits.
In effect, in seven scales, the reductions were identical in all three random splits.
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