Introduction to Predictive Modeling: From Correlation to Supervised…
Introduction to Predictive Modeling: From Correlation to Supervised Segmentation : :
Identifying Information Attributes
Use variables for Correlation
IG (Information Gain)
How much each variable purifies the model.
Creates a purer model
Segmenting Data by progressive attribute selection
Create simple tree
Measure pureness of each subset
Data corresponds to singular point
Easy to understand
Descending order to decision variable
Tested with Information Attributes
Why would it be more favorable to use an entropy model over a tree?
How do we better model a tree that does not have a binary class but rather 3, 4, or 5 classes?