Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| LightGBM× | Koku lēmumu pieņemšana (Decision Tree)× | Logistiskā regresija× | |
|---|---|---|---|
| Nozare≠ | Mašīnmācīšanās | Mašīnmācīšanās | Pētniecības statistika |
| Saime≠ | Machine learning | Machine learning | Process / pipeline |
| Izcelsmes gads≠ | 2017 | 1984 | 1958 |
| Autors≠ | Ke, G. et al. (Microsoft) | Breiman, Friedman, Olshen & Stone | David Roxbee Cox |
| Tips≠ | Gradient boosting decision tree ensemble | Recursive partitioning (if-then rules) | Method |
| Pirmavots≠ | 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 ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ | 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≠ | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | logit model, binomial logistic regression, LR |
| Saistītās≠ | 5 | 5 | 3 |
| Kopsavilkums≠ | 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. | A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf. | 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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