1. Next Generation Techniques: Trees, Network and Rules

2. What is a Decision Tree
Decision tree is a predictive model that can be viewed as a tree
Specifically, each branch of the tree is a classification question, and the leaves of the tree are partitions of the dataset with their classification

3. What is a Decision Tree (cont’d)
Some interesting things about the tree:
It divides up the data on each branch point without losing any of the data
The number of churners and non-churners is conserved as you move up or down the tree
It it pretty easy to understand how the model is being built
It is pretty easy to use this model

4. Applying Decision Tree to Business
Because of their tree structure and capability to easily generate rules, decision tree are the favored technique for building understandable models
Because of their high level of automation and the ease of translating decision tree models into SQL for deployment in relational databases, the technology has also proven to be easy to integrate with existing IT processes.

5. CART – growing a Forest and picking the best tree
CART which stands for Classification and Regression Trees, is a data exploration and prediction algorithm developed by Leo Breiman, Jerome Friedman, Richard Olshen, and Charles Stone
Predictors are picked as they decrease the disorder of the data
In building the CART tree, each predictor is picked based on how well it teases apart the records with different predictions.

6. CHAID
Another popular decision tree technology is CHAID or Chi-Square Automatic Interaction Detector.
CHAID is similar to CART, but it differs in the way that it chooses its splits.

7. What is Neural Network
When data mining algorithms are talked about these days, people usually talk about either decision trees or neural networks.
Of the two, neural networks have probably been of greater interest through the formative stages of data mining technology.

8. Are Neural Networks Easy to use?
A common claim for neural networks is that they are automated so that the user does not need to know that much about how they work, about predictive modeling, or even about the database in order to use them
There are many important design decisions that need to be made to effectively use a neural network, such as:
How should the nodes in the network be connected?
How many neuron-like processing units should be used?
When should ‘training’ be stopped in order to avoid overfitting?

9. Applying Neural Networks to Business
Neural Networks are very powerful predictive modeling techniques, but some of the power comes at the expense of ease-of-use and ease-of deployment
The model itself is represented by numeric value in a complex calculation that requires all of the predictor values to be in the form of a number
The output of the neural network is also numeric and needs to be translated if the actual prediction value is categorical.
For example, predicting the demand for blue, white, or black jeans for a clothing manufacturer requires that the predictor values blue, black, and white for the predictor color be converted to numbers.

10. Applying Neural Networks to Business (cont’d)
The neural network model have been successfully addressed in the following two ways:
The neural network is packaged up into a complete solution such as fraud prediction
The neural network is packaged up with expert consulting services.

11. What does a neural network look like
A neural network is loosely based on the way some people believe that the human brain is organized and how it learns.
There are two main structures of consequence in the neural network:
The node, which loosely corresponds to the neuron in the human brain
The link, which loosely corresponds to the connections between neurons (axons, dendrites, and synapses) in the human brain.

12. What does a neural network look like (cont’d)

14. Rule Induction
Rule induction is one of the major forms of data mining and is perhaps the most common form of knowledge discovery in unsupervised learning systems. It also perhaps the form of data mining that most closely resembles the process that most people think about when they think about data mining, namely ‘mining’ for gold through a vast database.

15. What to do with Rule
When the rules are mined out the database, the rules can be used either for understanding better the business problems that the data reflects or for performing actual prediction target.
Because there is both a left side and right side to a rule (antecedent and consequence) they can be used in several ways in your business:
Target the antecedent
Target the Consequent
Target based on accuracy
Target based on coverage
Target based on ‘Interestingness’

16. Rules versus Decision Trees
Decision trees also produce rules, but in a very different way than rule induction systems.
The main difference between the rules that are produced by decision trees and rule induction systems is as follows:
Decision trees produce rules that are mutually exclusive and collectively exhaustive with respect to the training database
Rule induction systems produce rules that are not mutually exclusive and might be collectively exhaustive

17. Another commonality between decision trees and rule induction systems
One other thing that decision trees and rule induction systems have in common is the fact that they both need to find ways to combine and simplify rules
In a decision tree, this can be as simple recognizing that if a lower split on predictor is more constrained than a split on the same predictor, both don’t need to be provided to the user – only the more restrictive one.
Rules from rule indication systems are generally created by taking a simple high-level rule, and then adding new constraints to it until the coverage gets to small so it is not meaningful.