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What is the role of the 'max_depth' parameter in a Decision Tree?

Practice Questions

Q1
What is the role of the 'max_depth' parameter in a Decision Tree?
  1. It determines the maximum number of features to consider
  2. It limits the number of samples at each leaf
  3. It restricts the maximum depth of the tree
  4. It controls the minimum number of samples required to split an internal node

Questions & Step-by-Step Solutions

What is the role of the 'max_depth' parameter in a Decision Tree?
  • Step 1: Understand that a Decision Tree is a model used for making decisions based on data.
  • Step 2: Know that the tree is made up of nodes, where each node represents a decision based on a feature.
  • Step 3: Realize that the tree can grow deeper by adding more nodes, which can lead to very specific decisions.
  • Step 4: Learn that 'max_depth' is a parameter that sets a limit on how many levels (or layers) the tree can have.
  • Step 5: Understand that limiting the depth helps to keep the model simple and prevents it from learning too much from the training data.
  • Step 6: Recognize that if the tree is too deep, it may perform well on training data but poorly on new, unseen data, which is called overfitting.
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