手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| ナイーブベイズ× | ランダムフォレスト× | |
|---|---|---|
| 分野 | 機械学習 | 機械学習 |
| 系統 | Machine learning | Machine learning |
| 提唱年≠ | 1997 | 2001 |
| 提唱者≠ | Mitchell, T. M. (textbook treatment) | Breiman, L. |
| 種類≠ | Probabilistic classifier (Bayes' theorem with conditional independence) | Ensemble (bagging of decision trees) |
| 原典≠ | Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072 | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| 別名≠ | Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| 関連 | 4 | 4 |
| 概要≠ | 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. | 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. |
| ScholarGateデータセット ↗ |
|
|