In this series of posts we will cover one of the widely used algorithms in machine learning. We will start with basics of segmentation and move further how segmentation can be evolved into a tree like structure.
Decision tree is a type of supervised learning algorithm that is mostly used in classification problems.In this technique, we split the population or sample into two or more homogeneous sets based on most significant differentiator in input variables.
What is Segmentation?
- Imagine a scenario where we want to run a SMS marketing campaign to attract more customers in the next quarter
- Some customers like to see high discount
- Some customers want to see a large collection of items
- Some customers are fans of particular brands
- Some customers are Male some are Female
- Divide them based on their demographics, buying patterns and profile related attributes
- One size doesn’t fit all
- Divide the population in such a way that
- Customers inside a group are homogeneous
- Customers across groups are heterogeneous
- Is there any statistical way of dividing them correctly based on the data