- Which will be the most performant features?
- Do we need all the features or will models perform better with fewer features?
- Which feature selection method will result in the best model performance?
- What impact does oversampling vs no oversampling vs Smote have on model performance?
- In particular, will this result in being able to minimise false negatives (type II errors)?
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- Borzooei, S., Briganti, G., Golparian, M. et al. Machine learning for risk stratification of thyroid cancer patients: a 15-year cohort study. Eur Arch Otorhinolaryngol (2023). https://doi.org/10.1007/s00405-023-08299-w
- Welch Dinauer, C.A., Michael Tuttle, , Robie, D.K., McClellan, D.R., Svec, R.L., Adair, C. and Francis, G.L. (1998), Clinical features associated with metastasis and recurrence of differentiated thyroid cancer in children, adolescents and young adults. Clinical Endocrinology, 49: 619-628. https://doi.org/10.1046/j.1365-2265.1998.00584.x