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The paper presents a novel method for reducing a multi-class Confusion Matrix into a 2 × 2 version enabling the use of the relevant performance metrics and methods like the Receiver Operator Characteristic and the Area Under the Curve for the assessment of different classification algorithms. The reduction method is based on class grouping and leads to a specific Confusion Matrix type. The developed method is then exploited for the assessment of several state-of-the-art machine learning algorithms applied on a customer experience metric.