paper
2020
Local & station forecasting
Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels) Journal
2020-05-20
004
May 2020
Ibrahim Gad, Doreswamy Hosahalli
International Journal of Computers and Applications
Compares classical machine learning classifiers and regressors on NCDC station weather data, finding CART, XGBoost and AdaBoost most accurate for classification.
A comparative study of prediction and classification models on NCDC weather data
In one sentence. Compares classical machine learning classifiers and regressors on NCDC station weather data, finding CART, XGBoost and AdaBoost most accurate for classification.
Labels
- Topic
- Local & station forecasting
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Local / station
- Data
- Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1080/1206212x.2020.1766769 OpenAlex:W3027204411
- Added
- 2026-10-22
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