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What is a key feature of Random Forests that enhances their robustness?
What is a key feature of Random Forests that enhances their robustness?
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What is a key feature of Random Forests that enhances their robustness?
Use of a single tree
Bootstrap aggregating (bagging)
Linear regression
Support vector machines
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Random Forests use bootstrap aggregating (bagging) to enhance robustness and reduce variance.
Questions & Step-by-step Solutions
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Q: What is a key feature of Random Forests that enhances their robustness?
Solution:
Random Forests use bootstrap aggregating (bagging) to enhance robustness and reduce variance.
Steps: 7
Show Steps
Step 1: Understand that Random Forests are a type of machine learning model used for classification and regression.
Step 2: Learn that they consist of many individual decision trees.
Step 3: Know that each decision tree is built using a random sample of the data.
Step 4: This random sampling is called 'bootstrap aggregating' or 'bagging'.
Step 5: Bagging helps to create diverse trees, which means they make different predictions.
Step 6: When you combine the predictions from all these trees, it reduces errors and makes the model more stable.
Step 7: This process of combining predictions from multiple trees enhances the robustness of the Random Forest model.
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