What is decision tree in SAS Enterprise Miner?
The Decision Tree node is located in the Model folder of the SAS Enterprise Miner toolbar. An empirical tree represents a segmentation of the data that is created by applying a series of simple rules. The final nodes are called leaves. For each leaf, a decision is made and applied to all observations in the leaf.
What is decision tree mining?
Decision Tree Mining is a type of data mining technique that is used to build Classification Models. It builds classification models in the form of a tree-like structure, just like its name. This type of mining belongs to supervised class learning. In supervised learning, the target result is already known.
How does decision tree work?
Decision trees use multiple algorithms to decide to split a node into two or more sub-nodes. The creation of sub-nodes increases the homogeneity of resultant sub-nodes. The decision tree splits the nodes on all available variables and then selects the split which results in most homogeneous sub-nodes.
Where is the decision tree used?
Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal, but are also a popular tool in machine learning.
How do you prune a decision tree in SAS?
Right-click on a node, and select Prune to prune the decision tree at that node. This removes all nodes beneath the selected node and turns that node into a leaf node.
Which is the best approach to Enterprise Miner?
The approach taken by SAS Enterprise Miner is a reasonable one since it has no business knowledge to base the outcome on other than what is provided — either pick the most likely outcome or the most valuable outcome based on your weights — but your best decisions will always incorporate your analytical needs.
How is a decision tree used in analytics?
6 Decision Trees for Analytics Using SAS Enterprise Miner Because a decision tree enables you to combine categories that have similar values with respect to the level of some target value, there is less information loss in collapsing categories together. This leads to improved prediction and classification results.
Which is the target variable in SAS Enterprise Miner?
If you do add Decision weights (either in the Decisions node or in the Input Data Source node), SAS Enterprise Miner will also generate a D _ which contains the ‘decision’ outcome based on the ‘most profitable’ or ‘least costly’ outcome.
When does a decision tree think something is bad?
When the Tree thinks the proportions are 30% bad and 70% good, it is more likely that some nodes classify observations as bad, and the misclassification rate can improve if these nodes are included in the Tree. With Adjust Priors, the Tree thinks there are 2% bad.