Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Кластеризація методом k-середніх× | Випадковий ліс× | |
|---|---|---|
| Галузь | Машинне навчання | Машинне навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 1967 (formalized 1982) | 2001 |
| Автор методу≠ | MacQueen, J. B.; Lloyd, S. P. | Breiman, L. |
| Тип≠ | Partitional clustering | Ensemble (bagging of decision trees) |
| Основоположне джерело≠ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Інші назви | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Пов'язані | 4 | 4 |
| Підсумок≠ | K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis. | 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. |
| ScholarGateНабір даних ↗ |
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