Methoden vergleichen
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| Regularisierte Logistische Regression× | Naive Bayes× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1996–2005 | 1997 |
| Urheber≠ | Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net) | Mitchell, T. M. (textbook treatment) |
| Typ≠ | Penalized classification model | Probabilistic classifier (Bayes' theorem with conditional independence) |
| Wegweisende Quelle≠ | Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ | Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072 |
| Aliasnamen≠ | penalized logistic regression, L1 logistic regression, L2 logistic regression, elastic net logistic regression | Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes |
| Verwandt≠ | 5 | 4 |
| Zusammenfassung≠ | Regularized logistic regression extends standard logistic regression by adding an L1 (lasso), L2 (ridge), or elastic net penalty to the log-likelihood, shrinking coefficients toward zero and preventing overfitting. It is the default choice for binary or multinomial classification when you want interpretable, sparse, or stable coefficient estimates in high-dimensional or collinear feature spaces. | Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate. |
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