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  "notes": "Uses a balance scale metaphor to illustrate the weighting of internal vs external drivers in a driver-based ML model.",
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      "text": "These external drivers change frequently, which trains the ML model to self-correct and enables a quick, optimized business response to shifting market conditions.",
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      "text": "Explain the story behind a forecast. Find material relationships between business drivers and financial outcomes. Model short-term effects of external market changes or disruptions. Adapt plans to market volatility and better map business complexity.",
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      "text": "As part of this strategy, ABB EL strengthened the ML algorithms with more complex external drivers that a business cannot directly control or influence, such as GDP or consumer sentiment. These external drivers change frequently, which trains the ML model to self-correct and enables a quick, optimized business response to shifting market conditions. Put simply, when one driver changes, multiple algorithms can automatically revise their forecasts with limited human oversight. This technique also provides the company with early-warning signals to model multiple future possibilities (see Figure 1).",
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      "text": "Source: Adapted From ABB",
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      "text": "Integrate Complex External Drivers Into ML Models",
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