A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms

Tjen-Sien Lim, Wei‐Yin Loh, Yu‐Shan Shih · Machine-mediated learning · 2000

Summary

Among decision tree algorithms with univariate splits, C4.5, IND-CART, and QUEST have the best combinations of error rate and speed, but C 4.5 tends to produce trees with twice as many leaves as those fromIND-Cart and QUEST.

Type: article
Published: 2000-09-01
Cited by: 1,267
References: 57
Access: Open access
DOI: https://doi.org/10.1023/a:1007608224229
OpenAlex: https://openalex.org/W2161349318
Semantic Scholar: https://api.semanticscholar.org/CorpusID:17030953

Keywords

Decision tree, Univariate, Logistic regression, Computer science, Statistics

References

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A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms

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