Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| LightGBM× | Mti wa Uamuzi× | Msitu Nasibu× | |
|---|---|---|---|
| Nyanja | Ujifunzaji wa Mashine | Ujifunzaji wa Mashine | Ujifunzaji wa Mashine |
| Familia | Machine learning | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2017 | 1984 | 2001 |
| Mwanzilishi≠ | Ke, G. et al. (Microsoft) | Breiman, Friedman, Olshen & Stone | Breiman, L. |
| Aina≠ | Gradient boosting decision tree ensemble | Recursive partitioning (if-then rules) | Ensemble (bagging of decision trees) |
| Chanzo asilia≠ | 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 ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Majina mbadala≠ | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Zinazohusiana≠ | 5 | 5 | 4 |
| Muhtasari≠ | 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. | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. |
| ScholarGateSeti ya data ↗ |
|
|
|