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What does pruning refer to in the context of Decision Trees?
What does pruning refer to in the context of Decision Trees?
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What does pruning refer to in the context of Decision Trees?
Adding more nodes to the tree
Removing nodes to reduce complexity
Increasing the depth of the tree
Changing the splitting criterion
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Pruning refers to the process of removing nodes from a Decision Tree to reduce its complexity and prevent overfitting.
Questions & Step-by-step Solutions
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Q: What does pruning refer to in the context of Decision Trees?
Solution:
Pruning refers to the process of removing nodes from a Decision Tree to reduce its complexity and prevent overfitting.
Steps: 5
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Step 1: Understand that a Decision Tree is a model used for making decisions based on data.
Step 2: Know that sometimes Decision Trees can become too complex by having too many branches or nodes.
Step 3: Realize that this complexity can lead to overfitting, which means the model performs well on training data but poorly on new data.
Step 4: Pruning is the process of simplifying the Decision Tree by removing some of these branches or nodes.
Step 5: The goal of pruning is to make the Decision Tree simpler, more general, and better at predicting new data.
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