Translations:PAVEsense - IRI Correlation/8/en: Difference between revisions

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Below we have also compared the "driving quality" indicator with the reference measurements. We analyse this with a "confusion matrix", a standard approach for classification problems. Quality is mainly predicted in neighbouring quality labels, as the confusion matrix shows. For example, of the roads whose true label is "very good", 96% are predicted as "good" or "very good".

Below we have also compared the "driving quality" indicator with the reference measurements. We analyse this with a "confusion matrix", a standard approach for classification problems. Quality is mainly predicted in neighbouring quality labels, as the confusion matrix shows. For example, of the roads whose true label is "very good", 96% are predicted as "good" or "very good".

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