Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
Re-examining Model Evaluation: The CRISP Approach
 The performance of prediction models can be judged using...
An Agile Approach to Data Science Product Development
 Introduction With the huge growth in machine learning over...
Wise Practitioner – Predictive Analytics Interview Series: Haig Nalbantian at Mercer – BIZ
 By: Eric Siegel, Founder, Predictive Analytics World for Business...
Is What You Did Ethical? Helping Students in Computational Disciplines to Think About Ethics
 In addition to this article, Dr. Priestly will also...

Original Content

Wise Practitioner – Predictive Workforce Analytics Interview Series: Greg Tanaka at Percolata

 In anticipation of his upcoming Predictive Analytics World for Workforce conference presentation, Big Data Driven Labor Scheduling, we interviewed Greg Tanaka, CEO at Percolata. View the Q-and-A below to see how Greg Tanaka has incorporated predictive analytics into the workforce of Percolata. Also, glimpse what’s in store for the new PAW Workforce conference. Q: How is a specific

Wise Practitioner – Predictive Workforce Analytics Interview Series: Michael Li at The Data Incubator

 In anticipation of his upcoming Predictive Analytics World for Workforce conference presentation, Finding Top Data Scientists for Your Organization: Optimize the Hiring Process with Analytics, we interviewed Michael Li, CEO at The Data Incubator.  View the Q-and-A below to see...

Four Ways Data Science Goes Wrong and How Test-Driven Data Analysis Can Help

 If, as Niels Bohr maintained, an expert is a person who has made all the mistakes that can be made in a narrow field, we consider ourselves expert data scientists.  After twenty years of doing what’s been...

In Predictive Analytics, Coefficients are Not the Same as Variable Influence

 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...

Oracle’s Ten Enterprise Big Data Predictions for 2016

 Companies big and small are finding new ways to capture and use more data. The push to make big data more mainstream will get stronger in 2016. Here are Oracle’s top 10 predictions: 1. Data civilians operate more...

Personalities That Are Barriers to Model Deployment (And How to Partner With Them) Part III: The Expert

 So you have gathered your data and completed your exploration and cleansing. You labored countless hours transforming the data and created a strong model that can revolutionize the way your company sees its clients, makes decisions and...

Wise Practitioner – Predictive Workforce Analytics Interview Series: Kathy Doan at Wells Fargo Bank

 In anticipation of her upcoming Predictive Analytics World for Workforce conference presentation, Beyond Traditional Turnover Creating Value by Quantifying the Impact of Attrition, we interviewed Kathy Doan, Vice President, Community Banking HR Insights & Analysis Group at Wells Fargo Bank. View the...

Wise Practitioner – Predictive Workforce Analytics Interview Series: Jonathon Frampton at Baylor Scott & White Health

 In anticipation of his upcoming Predictive Analytics World for Workforce conference presentation, Visualizing Organizational Movement for Opportunity Identification, we interviewed Jonathon Frampton, Director, People Analytics & Reporting at Baylor Scott & White Health. View the Q-and-A below to see how...

Mobile Analytics-Mining the Visit Experience of the Customer

 Mobile technology as part of the Big Data discussion is enabling data miners/data scientists to conduct analytics on information on the customer’s experience within a given location which otherwise was unavailable up until a few years ago....

The Devil’s Data Dictionary – Making Fun of Big Data

 Buy it on Amazon When Stéphane Hamel coined the description of Big Data as ‘That which doesn’t fit in an Excel spreadsheet’, I realized it was well past time for the datarati to have some fun poked...

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