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An engineer is building a system to classify customer feedback. They have three different models, each with varying performance on a test dataset: Model X has 85% accuracy, Model Y has 83% accuracy, and Model Z has 86% accuracy. The engineer combines these three models into an ensemble, where the final classification is determined by a majority vote of the individual models' predictions. Assuming the models tend to make errors on different, non-overlapping examples, what is the most likely outcome for the ensemble's performance?
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An engineer is building a system to classify customer feedback. They have three different models, each with varying performance on a test dataset: Model X has 85% accuracy, Model Y has 83% accuracy, and Model Z has 86% accuracy. The engineer combines these three models into an ensemble, where the final classification is determined by a majority vote of the individual models' predictions. Assuming the models tend to make errors on different, non-overlapping examples, what is the most likely outcome for the ensemble's performance?
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