Society of Actuaries (SOA) PA Practice Exam

What aspect does 'minbucket' directly control in a decision tree?

1. Structure of the tree

2. Size of the terminal nodes

3. Frequency of data points

4. Depth of the tree

The correct answer is: Size of the terminal nodes

The correct answer is that 'minbucket' directly controls the size of the terminal nodes in a decision tree. In decision tree algorithms, particularly in the context of creating rules for splitting the data at each node, 'minbucket' specifies the minimum number of observations that must be present in a terminal node (often referred to as a leaf node). This parameter helps to prevent overfitting by ensuring that each terminal node has a sufficient number of data points, which can improve the model's ability to generalize to unseen data. When 'minbucket' is set, it imposes a constraint on the tree-building process, influencing how the data is split. If a split would create a terminal node with fewer observations than specified by 'minbucket', that split will not be made. As a result, larger terminal nodes can provide more stable predictions. The other aspects mentioned, such as the structure of the tree, frequency of data points, and depth of the tree, are influenced by different parameters and decisions in the tree construction process. For example, the structure pertains to how the tree branches and organizes the splits made, while depth refers to how many layers of splits exist from the root to the terminal nodes. Thus, while these elements are crucial to