Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Brojesa modeļu vidējais svērums× | LASSO regresija× | |
|---|---|---|
| Nozare≠ | Bajesa metodes | Mašīnmācīšanās |
| Saime≠ | Bayesian methods | Machine learning |
| Izcelsmes gads≠ | 1999 | 1996 |
| Autors≠ | Hoeting, Madigan, Raftery & Volinsky | Tibshirani, R. |
| Tips≠ | Bayesian model averaging | Regularized linear regression (L1 penalty) |
| Pirmavots≠ | Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382–401. link ↗ | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ |
| Citi nosaukumi≠ | BMA, Bayesian model combination, Bayesian Model Ortalaması (BMA) | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | Bayesian Model Averaging (BMA), formalised as a tutorial by Hoeting, Madigan, Raftery and Volinsky in 1999, addresses model uncertainty by averaging over all plausible model specifications rather than selecting a single best model. Each candidate model receives a posterior probability that reflects how well it fits the data given a prior, and predictions or coefficient estimates are formed as weighted averages across the entire model space. This approach reduces the bias and overconfidence that arise when a single selected model is treated as the true one. | Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter. |
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