- Wise Practitioner – Predictive Analytics Interview Series: Alexander Wu at Nauto
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- Wise Practitioner – Predictive Analytics Interview Series: Daniel Rohr at Tracks GmbH
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- Wise Practitioner – Predictive Analytics Interview Series: Gopal Erinjippurath at Sust Global
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- Wise Practitioner – Predictive Analytics Interview Series: Edo van Uitert at ABN Amro
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By: Dean Abbott, Abbott Analytics and SmarterHQ, Inc.

Portions excerpted from Chapter 2 of his book Applied Predictive Analytics (Wiley 2014, http://amzn.com/1118727967) Successful predictive modeling is more than identifying the right algorithms. And, even though 60-90% of our time is spend on data preparation before deploying the first predictive model built from a new data set, successful predictive modeling goes well beyond effective

By Dean Abbott, Abbott Analytics and SmarterHQ, Inc.

In my last post, “Coefficients are not the same as variable influence”, I argued that coefficients in a linear regression model are useful but limited in answering the question, “which variables are most influential in model predictions?”...

By: Dean Abbott, Co-Founder & Chief Data Scientist, SmarterHQ

President, Abbott Analytics

When we build predictive models, we often want to understand why the model behaves the way it does, or in other words, which variables are the most influential in the predictions. But how can we tell which...

By: Dean Abbott, Co-Founder & Chief Data Scientist, SmarterHQ

President, Abbott Analytics

Excerpted and modified from Chapters 3 and 4 of Mr. Abbott’s book Applied Predictive Analytics, Wiley 2014 The Data Understanding stage of a predictive analytics project is intended to uncover the characteristics of the data available for...

By: Dean Abbott, SmarterHQ and Abbott Analytics

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By: Dean Abbott, President, Abbott Analytics

In my last two posts I described why overfitting predictive models is dangerous beyond the most obvious problem, namely that accuracy on new data is lower than expected. In the next few posts, I’ll describe how to...

By: Victoria Garment, Content Editor, Software Advice

As featured on Software Advice's Plotting Success blog

As featured on Software Advice's Plotting Success blog

Editor’s note: This article compares measures for model performance. Note that “accuracy” is a specific such measure, but that this article uses the word “accuracy” to generically refer to measures in general. In data mining, data scientists...

By: Dean Abbott, President, Abbott Analytics

Arguably, the most important safeguard in building predictive models is complexity regularization to avoid overfitting the data. When models are overfit, their accuracy is lower on new data that wasn’t seen during training, and therefore when these...

This speaker session is from Predictive Analytics World, September 30-October 1, 2013 in Boston, MA: (more…)

By: Dean Abbott, President, Abbott Analytics

Predictive Modeling competitions, once the arena for a few data mining conferences, has now become big business. Kaggle (kaggle.com) is perhaps the most well-known forum for modeling competitions, using a crowd-sourcing mentality: if more people try to...

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