BenchMark Quizzes by MDFT Pro

Select Method To Reduce Classification False Positives

Mark, the Student Success Analytics Lead at MDFT Pro, is fine-tuning a machine learning model that predicts which students are at risk of course failure to trigger early intervention support. MDFT Pro, a well-known training agency, wants to minimize false positive predictions because incorrectly identifying successful students as at-risk wastes valuable counselor time and resources, while also potentially discouraging students who are actually performing well. Mark needs to adjust the model to be more conservative in its at-risk predictions, accepting that some truly at-risk students might be missed (false negatives) in order to reduce the number of false alarms (false positives) for successful students.

What should Mark do to reduce false positive predictions in the student risk assessment model?

Choose the correct answer from the options below.

Explanations for each answer:

Modify the threshold value in favor of false positives is incorrect. Modifying the threshold to favor false positives would actually increase false positives rather than reduce them.
Modify the threshold value in favor of false negatives is correct. Adjusting the classification threshold to be more conservative (favoring false negatives) reduces false positives by requiring higher confidence for positive predictions.
Increase the number of training iterations is incorrect. More training iterations might improve overall accuracy but doesn't specifically address the balance between false positives and false negatives.
Include test data in the training data is incorrect. Including test data in training causes data leakage and overfitting, leading to unrealistic performance metrics rather than reducing false positives.

Learn more about classification metrics:

Classification Metrics

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Select Method To Reduce Classification False Positives