Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Lineārā regresija (ML)× | Random Forest× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1805–1809 | 2001 |
| Autors≠ | Legendre, A.-M. & Gauss, C.F. | Breiman, L. |
| Tips≠ | Supervised regression | Ensemble (bagging of decision trees) |
| Pirmavots≠ | Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed., Ch. 3). Springer. ISBN: 978-0-387-84858-7 | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Citi nosaukumi | ordinary least squares regression, OLS, least squares regression, multiple linear regression | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | Linear regression fits a straight-line relationship between one or more input features and a continuous numeric outcome by minimising the sum of squared prediction errors. As a machine-learning model it is trained on labeled examples and evaluated on held-out data, making it the simplest supervised learning baseline for any regression task. | 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. |
| ScholarGateDatu kopa ↗ |
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