Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
Effective Machine Learning Needs Leadership — Not AI Hype
 Originally published in BigThink, Feb 12, 2024.  Excerpted from The...
Today’s AI Won’t Radically Transform Society, But It’s Already Reshaping Business
 Originally published in Fast Company, Jan 5, 2024. Eric...
Calculating Customer Potential with Share of Wallet
 No question about it: We, as consumers have our...
A University Curriculum Supplement to Teach a Business Framework for ML Deployment
    In 2023, as a visiting analytics professor...

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Data Mining Group Updates PMML

 Version 4.2 of the Predictive Model Markup Language (PMML), which aims to make it easier to develop predictive analytics apps, is now available. The Data Mining Group, a vendor-led consortium of companies and organizations developing standards for statistical and data mining models, has released v 4.2 of the Predictive Model Markup Language (PMML), an XML

How bad data can lead to good decisions (sometimes)

 Before companies can profit from big data, they often must deal with bad data. There may indeed be gold in the mountains of information that firms collect today, but there also are stores of contaminated or “noisy”...

Machine Learning and Predictive Analytics Foster Growth

 Machine learning technology, which is defined in this ProgrammableWeb article, is starting to become a common component in many types of technology platforms. Machine learning has led to the advent of predictive analytics, which has been rapidly...

Warren Buffett Offers $1 Billion For Perfect March Madness Bracket

 It’s not every day that Brady gets sent packing, Manning sends potential travelers scrambling to the midwest and a Stanford grad makes news for freaking out on reporter Erin Andrews but that’s exactly what happened on Sunday....

CPG companies could improve business performance with better use of analytics, Accenture study finds

 The majority of consumer packaged goods (CPG) companies are failing to place analytics at the heart of their decision-making process, limiting their ability to improve the customer  experience and gain business advantage, a study by Accenture has...

Don’t Rely on Only One Technique

 A continuation of Dr. Elder’s talk on the top ten data mining mistakes and how to avoid them. The Top Ten Mistakes are covered in chapter 20 of the Handbook of Statistical Analysis & Data Mining Applications....

HR Should Hire ‘Scary’ Data People

 Too many people confuse reporting with analytics–and underinvest in making sure they are asking the right questions. Standard-issue reports about monthly employee turnover rates and average compensation per employee are important glances in the rearview mirror. But...

Poll Results: Text Analytics Use Shows No Significant Change

 Surprisingly, latest KDnuggets Poll did not find a significant change in Text Analytics use over the past 2 years. While 66% make some use of text analytics, only 19% use it on the majority of their projects....

Overspecialization throws data science dream teams off-balance

  Building a data science team is difficult enough, but growing one without losing the team’s effectiveness is a major challenge. Here’s why overspecialization is the wrong approach to growth. You’ve built a great data science team,...

Top 10 Data Mining Mistakes

 Dr. Elder gives his famous talk on the Top Ten Data Mining Mistakes. The Top Ten Mistakes are covered in chapter 20 of the Handbook of Statistical Analysis & Data Mining Applications. You can also view this...

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