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Which best exemplifies a use case for Binary Classification models?

  1. Predicting customer lifetime value

  2. Predicting whether an opportunity is won or lost

  3. Estimating time-to-close for an opportunity

  4. Forecasting revenue

The correct answer is: Predicting whether an opportunity is won or lost

The scenario that best exemplifies a use case for Binary Classification models is the prediction of whether an opportunity is won or lost. Binary Classification models are designed to categorize data into two distinct classes or outcomes. In this context, the outcome is clearly defined as either "won" or "lost," making it a perfect fit for this type of model. In contrast, predicting customer lifetime value, estimating time-to-close for an opportunity, and forecasting revenue typically involve predicting continuous numerical values or multiple class outcomes rather than a simple binary yes/no decision. These types of predictions do not align with the binary nature of classification tasks, thus reinforcing why the correct choice focuses on the clear dichotomy present in the winning or losing of an opportunity.