APA (7th ed.) Citation

Pargent, F., Pfisterer, F., Thomas, J., & Bischl, B. (2022). Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features. Computational statistics, 37(5), 2671-2692. https://doi.org/10.1007/s00180-022-01207-6

Chicago Style (17th ed.) Citation

Pargent, Florian, Florian Pfisterer, Janek Thomas, and Bernd Bischl. "Regularized Target Encoding Outperforms Traditional Methods in Supervised Machine Learning with High Cardinality Features." Computational Statistics 37, no. 5 (2022): 2671-2692. https://doi.org/10.1007/s00180-022-01207-6.

MLA (9th ed.) Citation

Pargent, Florian, et al. "Regularized Target Encoding Outperforms Traditional Methods in Supervised Machine Learning with High Cardinality Features." Computational Statistics, vol. 37, no. 5, 2022, pp. 2671-2692, https://doi.org/10.1007/s00180-022-01207-6.