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| Lineare Regression (ML)× | Entscheidungsbaum× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1805–1809 | 1984 |
| Urheber≠ | Legendre, A.-M. & Gauss, C.F. | Breiman, Friedman, Olshen & Stone |
| Typ≠ | Supervised regression | Recursive partitioning (if-then rules) |
| Wegweisende Quelle≠ | 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., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ |
| Aliasnamen≠ | ordinary least squares regression, OLS, least squares regression, multiple linear regression | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | 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. | 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. |
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