关于随机森林算法的问询：Bagging、ExtraTrees是否属于其范畴？

Great question—let’s break this down clearly since it’s easy to mix up these terms when diving into tree-based ensembles!

First: What’s Bagging? #

Bagging (short for Bootstrap Aggregating) is an ensemble learning framework, not a specific algorithm like random forest. Here’s the key breakdown:

• Random Forest is actually a specific implementation of the Bagging framework, built specifically for decision trees.
• The core of Bagging is creating multiple subsets of your training data via bootstrap sampling (randomly picking samples with replacement), training a model on each subset, then aggregating their predictions (voting for classification, averaging for regression).
• Random Forest adds an extra layer of randomness to Bagging: when building each decision tree, it also randomly selects a subset of features to consider at each split (instead of using all available features).

Then: What About ExtraTrees? #

ExtraTrees (Extremely Randomized Trees) is a variant of Random Forest, so it falls under the broader umbrella of random forest-style ensemble algorithms. The main differences from standard Random Forest are:

• It skips bootstrap sampling: every tree is trained on the full training dataset (no random sampling with replacement).
• When splitting nodes, it chooses random thresholds for candidate features instead of searching for the optimal split threshold (which is what standard Random Forest and regular decision trees do).
• This makes ExtraTrees faster to train and more robust to noise, but it can have slightly higher variance compared to standard Random Forest.

Quick Recap #

• Bagging: The foundational ensemble strategy that Random Forest relies on (but Bagging itself isn’t a "Random Forest algorithm example").
• ExtraTrees: A close cousin/variant of Random Forest, so it is part of the Random Forest family of algorithms.

内容的提问来源于stack exchange，提问作者robot1800