1. Some tools and a discussion.
GOALS
Some tools and a discussion.
Patrick Bailey, MS

2. Do you have a magical mystery tour?

3. Consider
… as the saying goes, “even a blind squirrel finds a nut.” But the best, most predictive models are fundamentally influenced by a modeler with expert knowledge and context of the problem.
Kuhn, Max, Johnson, Kjell, Applied Predictive Modeling, Springer, 2013

4. Process
Zumel et al, Practical Data Science with R, Manning 2014

5. Collaboration
Zumel et al, Practical Data Science with R, Manning 2014

6. An Observation
This challenge partly results from the scenario that current data mining is a data driven trial-and- error process (Ankerst 2002) where data mining algorithms extract patterns from converted data via some predefined models based on an expert’s hypothesis. Data mining is presumed to be an automated process producing automatic algorithms and tools without human involvement and the capability to adapt to external environment constraints…
In real world data mining, the requirement for discovering actionable knowledge in constraint-based context is satisfied by interaction between humans (domain experts) and the computerized data mining system. This is achieved by integrating human qualitative intelligence with computational capability.

7. A Facilitator Brings this Together
Facilitation applies to a structured meeting with an objective. This does not imply a predetermined decision.
Facilitators job
Keeps focus on objective
Captures what is said (parking boards, mind mapping software etc.)
Prods participants with leading and follow up questions
Keeps a healthy environment

8. Asch’s Experiment A C B

9. Value
Big Data Imperatives, Mohanty et al., Apress, 2013

10. Influence Diagrams
An influence diagram (ID) (also called a relevance diagram, decision diagram or a decision network) is a compact graphical and mathematical representation of a decision situation. It is a generalization of a Bayesian network, in which not only probabilistic inference problems but also decision making problems (following maximum expected utility criterion) can be modeled and solved.

11. Components of Influence Diagrams and Decision Trees
Represents relationships & sequence of context
Decision
Uncertainty
Objective/
Value

12. Relevance Arrows (arcs) in Influence Diagrams
Event A outcome is relevant to probability of Event B outcome
Outcome of Event A is known when making Decision B
Decision A is necessary to estimate probability of Event B
Decision A is made prior to Decision B A B
Relevance Arrows (arcs) in Influence Diagrams
Uncertainties become events after they happen

13. Optimized Growing Season Outcome
Actual Weather
Example for Farming
Weather Forecast
Optimized Growing Season Outcome
Seed Recommendations
Determine
Date to Plant
Market Preference
Select Crop

14. Considerations Iterative approach.
Do you believe the goal can be measured? Can the influences be measured?
Consider the data that supports the outcomes of decisions and events.
Consider data outside of your boundaries.
Does the data reveal a new influence? (Curiosity at play.)
Noticeable pattern (nodes with high degree of input)

15. Also…

16. GQM
Metric
Question
Goal

17. Recommended Reading