Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
Climate Tech Needs Machine Learning, Says PAW Climate Conference Chair
  Straight from the horse’s mouth – the founding...
Predictive Policing: Six Ethical Predicaments
  Originally published in KDNuggets. This article is based...
Measuring Invisible Treatment Effects with Uplift Analysis
  Models make predictions by identifying consistent correlations in...
Machine Learning: Business Leaders Must Take an Enlightening Look Under Its Hood (New Training Program)
  In this article, I identify unmet learner needs...

Original Content

Overcoming the Explainability Challenges of Machine Learning Models

 Some History Machine Learning Models, which have historically been referred to as predictive models, are not new. Any early practitioner in this field would emphasize that the two key deliverables of any model are as follows: its benefits to the business or organization Model Explainability (i.e. what is inside the model) The model benefits are

What’s In Your Basket?

 You’re finally upgrading to a top of the line smartphone. It has the features you desire, and you’re ready to pay for it. “Wait a minute,” the sales associate states. “You might want to consider an extended...

8 Things That Algorithms Can Do Better Than Humans

 We humans like to think that we are at the top of the evolutionary hierarchy. However, it is increasingly becoming clear that algorithms can execute some tasks better and more judiciously than humans can, or ever will...

Industrial Asset Optimization: Connecting Machines Directly with Data Scientists

 For more from this author, attend his virtual presentation, Industrial Asset Optimization:  Machine-to-Cloud/Edge Analytics, at Predictive Analytics World for Industry 4.0, May 31-June 4, 2020.  For industrial firms to realize the benefits promised by embracing Industry 4.0,...

Data Science Strategies for Banks and Credit Unions During COVID-19 and Beyond

 For more information on this topic, attend the virtual conference, Predictive Analytics World for Industry 4.0, May 31-June 4, 2020.  So many things are changing rapidly as the world responds to the risk from coronavirus. We know...

“If We Place Graduates Into the Private Sector, We Failed”: Why Universities and Companies Need to Rethink the Role of the PhD

 This is part 4 of a 5-part series on university/corporate partnerships in analytics and data science. In addition to this article, Dr. Priestley will also present on this topic at Predictive Analytics World for Business in Las...

Looking At The Numbers in COVID-19

 Like many of you, my focus during this crisis has been less on analytics and more about family, friends, etc. which on a more positive note seems to gain greater emphasis as we reassess our priorities.  But...

Some Thoughts on Analytics in a Post COVID-19 Environment

 In these most difficult times, the use of analytics is certainly not top of mind for most organizations unless it is being used to combat the virus. The challenging scenarios of meeting payroll and having access to...

Re-examining Model Evaluation: The CRISP Approach

 The performance of prediction models can be judged using a variety of methods and metrics. Some years ago, I was challenged to arrive at a set of rules that would provide both the analyst and marketer guidance...

An Agile Approach to Data Science Product Development

 Introduction With the huge growth in machine learning over the past few years, there is a lot of discussion, but few frameworks, on effective AI Project Management. Industry-standard frameworks for data analysis projects, like CRISP-DM, exist but...

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