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Differences Between Bagging and Random Forest
š The primary difference is the base estimator: Bagging allows any algorithm (like Decision Trees, SVMs, etc.), while Random Forest is strictly built using Decision Trees ().
š When making a Bagging ensemble with only Decision Trees, it is not equivalent to a Random Forest; the distinction lies in feature sampling.
š Bagging performs tree-level feature sampling: the subset of features used for the entire tree is decided once at the beginning.
š Random Forest performs node-level feature sampling: a new random subset of features is selected for consideration at *every node split* during tree construction, leading to greater randomness.
Feature Sampling Mechanism
š² In Bagging, if 2 out of 5 columns are sampled for a tree, that tree only uses those 2 columns for all its splits (e.g., using only and ).
š² In Random Forest, if 2 features are sampled, these features are re-sampled randomly at each node. For example, one node might split on , and the next node might split on .
š² This node-level sampling in Random Forest increases the randomness and diversity among the base estimators, which generally leads to better performance compared to standard Bagging.
Key Points & Insights
ā”ļø A key interview differentiator is that Bagging is a general ensemble technique, whereas Random Forest imposes the constraint of using Decision Trees exclusively.
ā”ļø If a Bagging Classifier is constructed using only Decision Trees, the distinction remains in the feature sampling method (tree-level vs. node-level).
ā”ļø Random Forest's node-level feature selection (introducing extra randomness) is the reason it often outperforms Bagging models in practical scenarios.
ā”ļø Always be prepared to explain the counter-question: If Bagging uses only Decision Trees, why isn't it a Random Forest? The answer is the feature sampling strategy.
šø Video summarized with SummaryTube.com on Nov 27, 2025, 10:04 UTC
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Full video URL: youtube.com/watch?v=l93jRojZMqU
Duration: 12:01
Get instant insights and key takeaways from this YouTube video by CampusX.
Differences Between Bagging and Random Forest
š The primary difference is the base estimator: Bagging allows any algorithm (like Decision Trees, SVMs, etc.), while Random Forest is strictly built using Decision Trees ().
š When making a Bagging ensemble with only Decision Trees, it is not equivalent to a Random Forest; the distinction lies in feature sampling.
š Bagging performs tree-level feature sampling: the subset of features used for the entire tree is decided once at the beginning.
š Random Forest performs node-level feature sampling: a new random subset of features is selected for consideration at *every node split* during tree construction, leading to greater randomness.
Feature Sampling Mechanism
š² In Bagging, if 2 out of 5 columns are sampled for a tree, that tree only uses those 2 columns for all its splits (e.g., using only and ).
š² In Random Forest, if 2 features are sampled, these features are re-sampled randomly at each node. For example, one node might split on , and the next node might split on .
š² This node-level sampling in Random Forest increases the randomness and diversity among the base estimators, which generally leads to better performance compared to standard Bagging.
Key Points & Insights
ā”ļø A key interview differentiator is that Bagging is a general ensemble technique, whereas Random Forest imposes the constraint of using Decision Trees exclusively.
ā”ļø If a Bagging Classifier is constructed using only Decision Trees, the distinction remains in the feature sampling method (tree-level vs. node-level).
ā”ļø Random Forest's node-level feature selection (introducing extra randomness) is the reason it often outperforms Bagging models in practical scenarios.
ā”ļø Always be prepared to explain the counter-question: If Bagging uses only Decision Trees, why isn't it a Random Forest? The answer is the feature sampling strategy.
šø Video summarized with SummaryTube.com on Nov 27, 2025, 10:04 UTC
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As an Amazon Associate, we earn from qualifying purchases

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