Supervised and unsupervised discretization of continuous features¶
Dougherty, J., Kohavi, & Sahami, M. (1995). Supervised and unsupervised discretization of continuous features. Proceedings of the Twelfth International Conference on Machine Learning.
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Primes¶
- Segmentation and Boundary Drawing
- Data Science: Feature discretization and binning transform continuous variables (age 0-100) into categorical bins (child, teen, adult, senior); the boundaries encode domain knowledge about life stages, with Dougherty, Kohavi, and Sahami (1995) systematically comparing equal-width, equal-frequency, and entropy-based discretization methods on classifier accuracy.
This sourceMorgan Kaufmann. Comparative study of equal-width, equal-frequency, and entropy-based discretization methods for continuous features in classifier pipelines.
- Data Science: Feature discretization and binning transform continuous variables (age 0-100) into categorical bins (child, teen, adult, senior); the boundaries encode domain knowledge about life stages, with Dougherty, Kohavi, and Sahami (1995) systematically comparing equal-width, equal-frequency, and entropy-based discretization methods on classifier accuracy.
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