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In Random Forests, how are individual trees typically trained?
Practice Questions
Q1
In Random Forests, how are individual trees typically trained?
On the entire dataset.
On a random subset of the data.
Using only the most important features.
With no data at all.
Questions & Step-by-Step Solutions
In Random Forests, how are individual trees typically trained?
Steps
Concepts
Step 1: Start with a complete dataset that you want to use for training.
Step 2: Randomly select a subset of the data. This subset is called a 'bootstrap sample'.
Step 3: Use this bootstrap sample to train one individual decision tree.
Step 4: Repeat Steps 2 and 3 multiple times to create many different trees, each trained on different random subsets of the data.
Step 5: Ensure that each tree is trained independently to promote diversity among the trees.
No concepts available.
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