Benchmark of Encoders of Nominal Features for Regression

• Type: Article in International Conference Proceedings Book
• Date: 2021

Authors

Diogo Seca (Author) Other; João Mendes Moreira (Author) FEUP

Conference proceedings International

Title: Trends and Applications in Information Systems and Technologies - Volume 1, WorldCIST 2021, Terceira Island, Azores, Portugal, 30 March - 2 April, 2021.

Pages: 146-155

World Conference on Information Systems and Technologies, WorldCIST 2021

1 April 2021 through 2 April 2021

Indexing

Scopus - 4 Citations

Other information

Authenticus ID: P-00T-XXJ

DOI: 10.1007/978-3-030-72657-7_14

Abstract (EN): Mixed-type data is common in the real world. However, supervised learning algorithms such as support vector machines or neural networks can only process numerical features. One may choose to drop qualitative features, at the expense of possible loss of information. A better alternative is to encode them as new numerical features. Under the constraints of time, budget, and computational resources, we were motivated to search for a general-purpose encoder but found the existing benchmarks to be limited. We review these limitations and present an alternative. Our benchmark tests 16 encoding methods, on 15 regression datasets, using 7 distinct predictive models. The top general-purpose encoders were found to be Catboost, LeaveOneOut, and Target. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10