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203.3.2 The Decision Tree Approach

Mechine learning With R

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

Main Questions

  • 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?

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