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| CatBoost× | Logistinen regressio× | |
|---|---|---|
| Tieteenala≠ | Koneoppiminen | Tutkimuksen tilastomenetelmät |
| Menetelmäperhe≠ | Machine learning | Process / pipeline |
| Syntyvuosi≠ | 2018 | 1958 |
| Kehittäjä≠ | Prokhorenkova, L. et al. (Yandex) | David Roxbee Cox |
| Tyyppi≠ | Gradient boosting on decision trees | Method |
| Alkuperäislähde≠ | Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Rinnakkaisnimet≠ | CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma | logit model, binomial logistic regression, LR |
| Liittyvät≠ | 5 | 3 |
| Tiivistelmä≠ | CatBoost is a gradient boosting algorithm, introduced by Prokhorenkova and colleagues at Yandex in 2018, that handles categorical variables natively and uses ordered target encoding to avoid label leakage. By building an additive ensemble of trees while permuting the data order at each iteration, it is often superior to XGBoost and LightGBM on category-heavy data. | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. |
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