Discuss the importance of data : The stopping criteria for controlling tree growth

The video tutorial explains how to control the growth of decision trees to prevent overfitting. It introduces three methods: setting a minimum number of observations required to split a node, specifying the minimum number of observations at a leaf node, and defining the maximum depth of the tree. These methods help in determining when the tree-building process should stop, ensuring the tree does not grow excessively and maintains accuracy.

Why is it important to have stopping criteria in decision trees?

• To ensure the tree is as large as possible

• To prevent overfitting

• To make the tree grow indefinitely

• To reduce the number of nodes

What happens if the minimum number of observations required to split a node is not met?

• The tree growth will stop at that node

• The node will be deleted

• The node will split anyway

• The tree will continue to grow

How does setting a minimum number of observations at a leaf node affect tree growth?

• It makes the tree grow faster

• It ensures every node has the same number of observations

• It prevents splits that result in too few observations in a leaf

• It allows unlimited splits

What is the effect of setting a maximum depth for a decision tree?

• It allows the tree to grow indefinitely

• It limits the number of splits based on depth

• It ensures all nodes have the same depth

• It increases the number of leaf nodes

Which method helps in controlling the growth of a decision tree by limiting its depth?

• Random node selection

• Maximum depth of the tree

• Minimum observations at a leaf node

• Minimum observations for node split