ML Success Should Be Measured in Business Value, Not Just Accuracy
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Machine learning models are often evaluated on technical metrics like accuracy rather than business metrics like revenue, which fails to show their true business value. This can sabotage ML projects.
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Business metrics like profit and ROI directly measure value and should be prioritized over technical metrics when reporting to stakeholders.
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You can calculate business value by assigning costs to false positives and false negatives based on their business impact.
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Less accurate ML models can sometimes create more value by making better trade-offs between false positives and negatives.
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Reporting absolute business value helps stakeholders make informed decisions about deploying ML models. Technical metrics alone leave them unable to properly evaluate value.