Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Multinomiaalinen logistinen regressio× | Random Forest× | |
|---|---|---|
| Tieteenala≠ | Tilastotiede | Koneoppiminen |
| Menetelmäperhe≠ | Regression model | Machine learning |
| Syntyvuosi≠ | 1966–1974 | 2001 |
| Kehittäjä≠ | Cox (1966); Theil (1969); formalized by McFadden (1974) | Breiman, L. |
| Tyyppi≠ | Generalized linear model | Ensemble (bagging of decision trees) |
| Alkuperäislähde≠ | Agresti, A. (2002). Categorical Data Analysis (2nd ed.). Wiley-Interscience. ISBN: 978-0471360933 | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Rinnakkaisnimet | polytomous logistic regression, softmax regression, multinomial logit, nominal logistic regression | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Liittyvät | 4 | 4 |
| Tiivistelmä≠ | Multinomial logistic regression extends binary logistic regression to outcomes with three or more unordered categories. It models the log-odds of each category relative to a chosen reference category as a linear function of the predictors, and estimates all parameters simultaneously via maximum likelihood. It is the standard choice when the dependent variable is nominal with multiple levels. | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. |
| ScholarGateAineisto ↗ |
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