Discuss the significance of the 'true positive' rate as depicted in a confusion matrix for a binary classifier and how it relates to the model's recall and precision. Explain what it means for a model's performance if it has high recall but low precision, and provide a business scenario where this might be acceptable or even preferred. Include a hypothetical example of how this understanding can influence the decision to adjust a model's threshold for predicting positive instances. Grading Scheme: Understanding of True Positives (30%) Implications of Recall and Precision (30%) Application to Business Scenarios (40%)