Correcting Imbalance in Synthetic Medical Data Generation
Based on the provided scenario, analyze the team's initial data generation process. Explain why it failed to produce a balanced dataset and propose a revised, step-by-step process that would allow them to achieve their goal of 100 notes per category.
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
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A data scientist is using a large language model to generate synthetic examples of customer feedback for a classification task with two categories: 'Positive Sentiment' and 'Negative Sentiment'. After generating 1,000 examples, they find that 900 are 'Positive Sentiment' and only 100 are 'Negative Sentiment'. Which of the following strategies provides the most direct control to create a new, perfectly balanced dataset of 1,000 examples (500 of each category) during the generation process?
Correcting Imbalance in Synthetic Medical Data Generation
A machine learning engineer needs to generate a perfectly balanced synthetic dataset for a sentiment classification task (50% positive, 50% negative). To achieve this, they decide to reverse the typical generation process to gain direct control over the class distribution. Arrange the following steps in the correct logical order to implement this technique for one class, such as 'Positive'.