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What does the term 'bagging' refer to in the context of Random Forests?
Practice Questions
Q1
What does the term 'bagging' refer to in the context of Random Forests?
Using a single Decision Tree for predictions
Combining predictions from multiple models
Randomly selecting features for each tree
Aggregating predictions by averaging
Questions & Step-by-Step Solutions
What does the term 'bagging' refer to in the context of Random Forests?
Steps
Concepts
Step 1: Understand that 'bagging' is short for 'Bootstrap Aggregating'.
Step 2: Know that it involves creating multiple versions of a model.
Step 3: Each version of the model is trained on a different random sample of the data.
Step 4: After training, each model makes its own predictions.
Step 5: The final prediction is made by combining the predictions from all models, usually by averaging or voting.
Step 6: This process helps to reduce errors and improve the overall performance of the model.
No concepts available.
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