Predictive Analytics World New York 2016 Supercharging with Ensemble Models.

The Wisdom of Crowds (Photo credit: Wikipedia)

Dean Abbott taught this class on Ensemble models.

One of the in class demonstrations was an example from The Wisdom of Crowds. He passed around a bottle with some cereal in it. Everyone guessed, and then he averaged the guesses.

Two people were closer than the true answer, but an Ensemble model (An average of all of our individual guesses based on our internal model of the bottle and the size of the cereal.)

There were also hands-on demonstrations with Salford Systems predictive modeler. You can find out more about the tool at this link

Dean is a thorough instructor, and clearly could educate all of us on the various ways of doing predictive modeling.

He talked about Logistic and Linear regression, decision trees, random forests, and how to combine these specific models with various options as an Ensemble model.

He touched just briefly on deep-learning.

I look forward to hearing from him again, I think every time I would be able to hear from him I would learn something new.

Dean recommended to read his book: Applied Predictive Analytics Principles I look forward to starting to read this on the flight back.

It has been an exciting time in New York. Whenever I attend these conferences, and workshops I always feel like the more I learn, the less I know.

I did take a few pictures, and made a few tweets about one day speaking at a future event. I think I have a lot to learn to be on par with these speakers.

Continuous learning is the key to expertise.

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Predictive Analytics World New York 2016 Day 2

Daniel Kahneman (Photo credit: Wikipedia)

Day 2 kicked off with a bang.


John Elder speaking about the way we think and perceive solving problems. Daniel Kahneman's concept of System 1 thinking versus System 2 thinking applies to predictive analytics because many times our System 1 thinking can overwhelm our System 2 thinking.


The excellent book Fast and Slow thinking by Kahneman is a great overview of these concepts.

Dean Abbot and Karl Rexler followed up the great kick-off with a Question and Answer session. A couple of the more interesting questions were:

"How do you merge Analysis frameworks with Agile Frameworks?" (Answer: It's hard)

and

"Should Data Science report to business units trying to solve a problem, or Information Technology departments where the same resources can be shared and leveraged across the organization?" (Answer: It depends on your organization and support for Data Science.)

Pasha Roberts gave an amazing overview of Talent Analytics approach to understand workforce movement and flow of employees through an organization. Agent based models, Markov-chains and directed Graphs were the details of how to solve this problem. I was on cloud 9 it is so refreshing to hear about applications of these techniques to solving business problems. Most people I speak to about these techniques I lose quickly. :)

A few more vendor presentations, and some Q&A sessions as well as talks on Design thinking and graph analysis of food recommendations rounded out the rest of the day.

Tomorrow is more workshops. I will be attending the workshop by Dean Abbott on Ensemble Models.

New York has been a great trip, it is always a great experience to spend time with professional peers that are wrestling with some of the same problems and challenges.

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Predictive Analytics World New York 2016 - Day 1

English: Phases of the CRISP-DM process Français : Phases du processus CRISP_DM (Photo credit: Wikipedia)

Day 1 was exciting.


Great keynote by Eric Siegel on understanding whether a discovery you have made is BS or not.

(Bad Science for those of you not there.)



Vast search introduces new considerations in working with predictive models in large data sets, because, in a large enough data set almost any conditions can be found.

After all, "If you torture your data long enough, it will confess to anything."


Really knowing whether any finding is valid is very important when dealing with big data.

I followed the track related to Uplift Modeling.

I will need to go through Eric's book on Uplift modeling to best understand it but the examples provided in the sessions on Day 1 were enough to not only whet the appetite, but also dive in and do some experiments.

I also met with the fine people at Elder research, it turns out we both worked on a very similar government project some years ago.

They have done some research, and applications on the integration of CRISP-DM with the Agile framework. This is most intriguing I have to follow up with them to learn more about how they married such different methodologies.

At the end of day 1 I signed up for dinner with strangers, there were three groups of about 8 folks each who were sent to various restaurants throughout New York City. We had some good conversations about our individual struggles to bring Predictive Analytics to the masses.

I made some new networking connections, and look forward to staying in touch with some really positive professionals.

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Predictive Analytics World New York 2016 pre-workshop

English: A principal Component Analysis Example with air quality data available with R Français : Un exemple d'Analyse en Composante Pricipale avec les données de la qualité de l'air disponibles dans R (Photo credit: Wikipedia)

I am in New York City attending the Predictive Analytics World conference.

Max Kuhn is the speaker at the first session I attended "R for Predictive Modeling: A hands-On Introduction".


Max is a great speaker, and very knowledgeable about the topic. He has loads of experience in doing predictive modeling.


Every time I attend a course like this one, I learn that there is so much more I have to learn.

We covered details of topics covering
Principal component analysis, Feature selection, exploratory data analysis.

Various regression capabilities.

Many of the topics he covered he provided links to other blog posts, and github presentations that he has done it before.

These sessions are always fascinating seeing the variety of ways in which Predictive Analytics is used in many different environments.

I was able to speak to a few folks about the ways in which they are applying predictive analytics, and I look forward to more sessions as the conference proper kicks off tomorrow morning.


Most of the tracks on my schedule for tomorrow are all around uplift modeling I look forward to learning more both about how that works, and how to apply it.

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Is Analytics a Noun or a Verb?

English: The syntax tree of noun phrase "my neighbour's daughter-in-law" with layered determiner analysis. (Photo credit: Wikipedia)

Is Analytics the name of your department, or do you actually "do" Analytics?

Doing analytics requires you to look at your data, apply some logic, and make or support making a decision with the data.


For many years I have built and maintained analytical platforms. These platforms had the core of a Business Intelligence architecture with some one-offs for the occasional "sophisticated" analysis as needed. I was not specifically doing analytics during this time. I knew many of the tools and techniques that were being applied. At times, I was even the one writing the SQL queries to pull the data together to load into SAS for statistical modeling. However, I rarely took it so far as to actually do the Analytics myself. That was not my role.

Now I am in a position where I am the one doing the Analytics, and I see and recognize the impedance mismatch that occurs when I use the term analytics, versus when some people use the same term.

Data Analytics is a very overloaded term in today's environment.  Yet as sophisticated as we may be in evolving from our ancestors simple things still make a big difference.


Using incredibly simple definitions:
A Noun is a person, place or thing.

A Verb is an action, or state of being.


Analytics can be a noun. "I am in charge of the Analytics department!"

Analytics can also be a verb. "I applied Analytics to the data until it gave me the answer!"

Analysis, or analytical thinking is a way of learning from and understanding the data that we have available to us in order to solve a specific problem or answer a specific question.

I think how this word evolved to be a noun is that there have been times where people with analytical skills(verb) were gathered together in one place. In order to have a question answered you had to go to the Analytics department (now it is a noun - place.)

As this place evolved, the people doing the analysis needed support, programmers, managers, project managers, special coders,etc.

Now you can say you work in Analytics and mean the department. This carries some clout with it, because it sounds as if you have the skills and capabilities of those doing the analysis.

Not necessarily. You may learn some valuable things, and through the natural sequence of apprenticeship you may be able to be the one "doing analytics" at some point.

To me, Analytics is a Verb, and it should only be a verb. Using it in any other context is a disservice to the word.

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