BUS5PA Predictive Analysis Building and Evaluating Assignment 2022

BUS5PA Predictive Analysis
Building and Evaluating
Predictive Models Using SAS
Enterprise Miner
Assignment 2
By
(Name of Student)
(Institutional Affiliation)
1.
Decision tree based modelling and analysis.
2.A: After dragging the Organics dataset to the Organics
diagram, we connect the Data Partition node to the Organics
dataset. 50% of the data is utilized for training while the
remaining 50% of the data is used for validation (Appendix –
Figure 2.A). Training set is used to build a set of models while
Validation set is utilized to select the best model created from
the Training set.
Figure 2.A (2) – Adding Data Partition to the Organics data source.

2.B: A Decision Tree is then connected to the Data Partition
node (Appendix – Figure 2.B)
2.C.1: The number of leaves in an Optimal tree is 29 based on
Average Square Error as the subtree assessment plot. This
Decision Tree has been created using Average Square Error
(ASE) as the subtree Assessment Measure (Appendix – Figure
2.C.). The assessment method specifies the type of method
used to select the best tree. ASE opts for the tree that produces
the smallest average square error.
Figure 2.C.1 – Optimal Tree based on Average Square error as the
Subtree Assessment.
2.C.2: Variable DemAge was used for the first split as this is the
variable which ensures the best split in terms of ‘Purity’
(Appendix – Figure 2.C.2).
Based on Logworth of each input variable, the competing
splits for the first split (DemAge) for the first decision tree are
DemAffl and DemGender. Logworth is measure of Entropy,
which indicates which variable can create the most
homogenous subgroups.

Figure 2.C.2 – Logworth of Input Variables
2.D.1: The maximum branches of the second decision tree has
been changed to 3. This means the subsets of the splitting rules
are divided into 3 branches (Appendix – Figure 2.D.1).
2.D.2: The second Decision Tree has been created using
Average Square Error (ASE) as the subtree Assessment
Measure (Appendix – Figure 2.D.2). The assessment method
specifies the type of method used to select the best tree. ASE
opts for the tree that produces the smallest average square
error.
Figure 2.D.2 (2) – Adding the second Decision Tree node.
2. D.3: The optimal tree for Decision Tree 2 using Average
Square Error as the model assessment statistic contains 33
leaves.