AI models
Decision tree (classification)
Training compute
6.3×10¹³ FLOP
Parameters
12K
Published
Dec 8, 2001
Decision tree (classification) is an AI model developed by Mitsubishi Electric Research Labs and Compaq CRL (United States), first published in December 2001. It works in the vision domain, on tasks such as face recognition. It counts among the frontier models: the systems trained with the most compute of their moment.
Training it took an estimated 6.3×10¹³ FLOP of compute (estimation method: operation counting). The model has 12,000 parameters. It was trained on roughly 753.6K datapoints.
The reference paper has 23,449 citations. Epoch AI rates the confidence of this record as likely.
Full record
- Organization
- Mitsubishi Electric Research Labs, Compaq CRL
- Country of organization
- United States
- Domain
- Vision
- Task
- Face recognition
- Training compute
- 6.3×10¹³ FLOP
- Compute estimation method
- Operation counting
- Parameters
- 12,000
- Dataset size
- 753.6K
- Citations
- 23,449
- Epoch confidence
- Likely
SourceEpoch AI, 'AI Models'. Published online at epoch.ai. Retrieved 2026-07-29 from https://epoch.ai/data/ai-models. Licensed under CC BY 4.0.
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