1. ISQS 7342 Dr. zhangxi Lin By: Tej Pulapa

2. DT in Forecasting Targeted Marketing - Know before hand what an online customer loves to see or hear about. Credit approval – I can forecast who is good to pay you back. Medical Diagnosis – my model will tell you if you have chances to get cancer genetically. Searching for High Info Gains - Given something I am trying to predict, it is easy to ask the computer to find which attribute has highest information gain for it.

3. DT in Classification Models Training Data Classification Algorithms Classifier (Model) Unseen Data The optimum extent to which the model predicts accurately demands Training Data set to be representative of the unseen data. So is it just about the available data?

4. Training Set Error For each record, follow the decision tree to see what it would predict For what number of records does the decision tree’s prediction disagree with the true value in the database? This quantity is called the training set error. The smaller the better.

5. DT can be used to analyze cross-sectional and time series data Reworking of the data can be done in certain situations – for e.g., In direct marketing, there is a need to derive customer measures for recency, frequency, and monetary value from transactional data based on purchase interactions

6. In order to obtain valuable marketing rules, decision tree induction technique is used to analyze purchase- transaction histories, customer profiles, and product information. The extracted marketing rules are stored in a marketing-rule base and are used for real-time personalized-advertisement selection when customers visit the Internet store

7. The process of recommendation rule extraction consists of four steps, (1)target variable generation (2) data partitioning (3) decision tree construction (4) recommendation rule selection.