Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Päätöspuu× | Regressio- ja tasoitussplinit× | |
|---|---|---|
| Tieteenala | Koneoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 1984 | 1996 |
| Kehittäjä≠ | Breiman, Friedman, Olshen & Stone | Spline regression literature; P-splines by Eilers & Marx |
| Tyyppi≠ | Recursive partitioning (if-then rules) | Piecewise-polynomial nonparametric regression |
| Alkuperäislähde≠ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ | Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89–121. DOI ↗ |
| Rinnakkaisnimet≠ | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | splines, cubic splines, natural splines, smoothing splines |
| Liittyvät≠ | 5 | 4 |
| Tiivistelmä≠ | 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. | Regression splines model a nonlinear relationship by fitting piecewise polynomials that join smoothly at a set of points called knots. Cubic and natural splines are the most common, and smoothing splines add a roughness penalty that automatically balances fit against smoothness. Splines are the standard flexible building block for univariate nonlinear regression and the basis of generalized additive models. |
| ScholarGateAineisto ↗ |
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