Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Regressioni ya Mfumo wa Mlinganyo wa Kawaida× | Regresi Laini (ML)× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Mashine | Ujifunzaji wa Mashine |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 1996 | 1805–1809 |
| Mwanzilishi≠ | Breiman, L. (bagging framework) | Legendre, A.-M. & Gauss, C.F. |
| Aina≠ | Ensemble of linear models | Supervised regression |
| Chanzo asilia≠ | Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. DOI ↗ | 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 |
| Majina mbadala | bagged linear regression, aggregated linear regression, stacked linear models, bootstrap-aggregated OLS | ordinary least squares regression, OLS, least squares regression, multiple linear regression |
| Zinazohusiana≠ | 6 | 5 |
| Muhtasari≠ | Ensemble Linear Regression combines multiple ordinary least-squares models — each fitted on a different bootstrap sample or feature subset — and averages their predictions. The technique, grounded in Breiman's bagging framework (1996), reduces variance and improves predictive stability compared with a single linear regression fit, while retaining the interpretability of linear assumptions. | 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. |
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