The Decision Tree Approach
In previous section, we studied about Decision Trees in r : Segmentation
- The aim is to divide the whole population or the data set into segments
- The segmentation need to be useful for business decision making.
- If one class is really dominating in a segments
- Then it will be easy for us to classify the unknown items
- Then its very easy for applying business strategy
- For example:
- It takes no great skill to say that the customers have 50% chance to buy and 50% chance to not buy.
- A good splitting criterion segments the customers with 90% -10% buying probability, say Gender=“Female” customers have 5% buying probability and 95% not buying
Example Sales Segmentation Based on Age
Example Sales Segmentation Based on Gender
- Ok we are looking for pure segments
- Dataset has many attributes
- Which is the right attribute for pure segmentation?
- Can we start with any attribute?
- Which attribute to start from? – The best separating attribute
- Customer Age can impact the sales, gender can impact sales , customer place and demographics can impact the sales. How to identify the best attribute and the split?
In next section, we will be studying about How Decision tree Splits works?