The following pages and posts are tagged with
| Title | Type | Excerpt |
|---|---|---|
| AdaBoost | Page | * Short for Adaptive Boosting, AdaBoost is another ensemble algorithm. ## Differences with decision trees * In random forests, we built complete trees each time, but in AdaBoost, each tree only consists of a node and two leaves (which is called a stump). * Another difference to RF is that... |
| Gradient-boosted trees | Page | ## Gradient-boosted trees * This algorithm can also be used for regression and classification. * It builds trees one after another, each new tree fixing the problems of the previous one. * It involves no randomization by default. * It uses shallow trees (maximum depth about 5). Therefore requires less... |
| Random Forests | Page | ## Ensemble algorithms * Random forests is an ensemble algorithm. * They combine multiple ML algorithms to create a more powerful one. * In competitions, ensemble algorithms are usually the winners. * Two most common ensemble algorithms are _random forests_ and _gradient boosted decision trees_. ## Random forests * A... |