To understand which layer of a decision tree tells you how a series of connected factors lead to a final outcome, we need to consider the structure and purpose of different layers in a decision tree.
A decision tree consists of nodes and branches, where each node represents a test or decision point based on a certain feature in the dataset. The tree starts from a root node and branches out into internal nodes and eventually into terminal nodes called leaves.
1. The Top Layer (Root Node):
- The top layer is the root node of the tree. This node contains the initial feature that the decision tree considers to start the branching process.
- It does not provide a final outcome but rather the first decision point in the tree.
2. The Bottom Layer (Leaf Nodes):
- The bottom layer consists of the leaf nodes. These nodes are the terminal points of the decision tree where the final outcomes or predictions are made.
- They summarize the results of all previous decisions and splits, effectively providing the final answer based on the paths taken through the tree.
3. Any Layer with a Threshold Value over 5%/15%:
- These references to threshold values indicate decision points or nodes where certain criteria based on threshold values are applied.
- While these nodes can be important in the decision-making process, they are not the final outcome but part of the decision path leading to it.
Since the leaf nodes in the bottom layer are the points at which the tree provides its final outcomes, the correct answer is:
The bottom layer.
