AI & Machine Learning

When Accuracy Lies: Rethinking Metrics in Imbalanced Multiclass Models

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Accuracy as a metric fails to provide reliable insights in multiclass models with imbalanced datasets, often exceeding theoretical bounds due to its inherent bias towards majority classes. This misalignment highlights the need for developers to carefully choose between micro, macro, or weighted averaging strategies based on their specific evaluation needs and dataset characteristics.

Read the full article at Towards AI - Medium

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When Accuracy Lies: Rethinking Metrics in Imbalanced Multiclass Models

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