Artificial Intelligence Applications in Engineering (MUH-920), week 7 of 14: interactive lab
Prof. Dr. Utku Kose, Süleyman Demirel University
Part A generates strength data from a known law, shaped after Abrams' water-cement rule [1], adds noise, and lets polynomial, nearest-neighbour and tree regressors fit it; training error, error on new data and a five-fold validation curve show where overfitting starts [2]. Part B is a leakage detective with six candidate features from engineering projects [3].
Five-fold validation curve over the complexity setting of the chosen model. The dashed line marks the current setting.
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] Abrams, D. A. (1918). Design of Concrete Mixtures (Bulletin 1). Structural Materials Research Laboratory, Lewis Institute, Chicago.
[2] Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Springer. https://doi.org/10.1007/978-0-387-84858-7
[3] Kaufman, S., Rosset, S., Perlich, C., & Stitelman, O. (2012). Leakage in data mining: Formulation, detection, and avoidance. ACM Transactions on Knowledge Discovery from Data, 6(4), 15. https://doi.org/10.1145/2382577.2382579
[4] Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55-67. https://doi.org/10.1080/00401706.1970.10488634
[5] Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324