Artificial Intelligence Applications in Engineering (MUH-920), week 8 of 14: interactive lab
Prof. Dr. Utku Kose, Süleyman Demirel University
Part A generates classifier scores for failures and normal cases with adjustable separation and imbalance. A threshold slider updates the confusion matrix, precision, recall, F1 and the expected cost, and the ROC and precision-recall curves show the current operating point [1, 2]. Part B asks for metrics computed from confusion matrices of engineering scenarios.
Six questions with instant feedback. Rate your confidence before checking each answer.
Answers are saved in this browser only. The export creates a Markdown learning log for your portfolio.
After the export, continue with the discipline challenge and the weekly task in the week overview.
[1] Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. https://doi.org/10.1016/j.patrec.2005.10.010
[2] Saito, T., & Rehmsmeier, M. (2015). The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLOS ONE, 10(3), e0118432. https://doi.org/10.1371/journal.pone.0118432
[3] Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321-357. https://doi.org/10.1613/jair.953