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Aggregation Methods in Ensemble Learning
Choosing an Aggregation Strategy
A team is building a system to classify customer support tickets as 'Urgent' or 'Not Urgent'. They have trained three separate predictive models with the following characteristics. Based on this information, which method of combining the models' outputs would likely yield the best performance, and why is it superior to a simple 'majority rules' approach in this specific scenario?
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
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Analysis in Bloom's Taxonomy
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Choosing an Aggregation Strategy
An ensemble of five models provides predictions for a binary classification task ('Class A' or 'Class B'). For a particular data point, the predictions are: [Model 1: 'Class A', Model 2: 'Class A', Model 3: 'Class B', Model 4: 'Class B', Model 5: 'Class B']. If you know that Model 1 has a significantly higher historical accuracy than the other four models, which aggregation method is specifically designed to leverage this performance information and give more influence to Model 1's prediction?