Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Gradient Boosting× | LightGBM× | Logistiskā regresija× | |
|---|---|---|---|
| Nozare≠ | Mašīnmācīšanās | Mašīnmācīšanās | Pētniecības statistika |
| Saime≠ | Machine learning | Machine learning | Process / pipeline |
| Izcelsmes gads≠ | 2001 | 2017 | 1958 |
| Autors≠ | Friedman, J. H. | Ke, G. et al. (Microsoft) | David Roxbee Cox |
| Tips≠ | Ensemble (sequential boosting of decision trees) | Gradient boosting decision tree ensemble | Method |
| Pirmavots≠ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q. & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems (NeurIPS) 30, 3146–3154. link ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Citi nosaukumi≠ | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting | logit model, binomial logistic regression, LR |
| Saistītās≠ | 5 | 5 | 3 |
| Kopsavilkums≠ | Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost. | LightGBM is Microsoft's gradient boosting decision tree implementation, introduced by Ke and colleagues in 2017, that grows trees leaf-wise and bins features into histograms for speed. On large datasets it is much faster than XGBoost while retaining strong predictive accuracy. | 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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