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 – Manufacturing Predictive Analytics Interview Series: Edward Crowley at The Photizo Group, Inc.

 In anticipation of his upcoming Predictive Analytics World for Manufacturing conference presentation, Predictive Analytics – What is 2% Worth, we interviewed Edward Crowley, CEO at The Photizo Group, Inc. View the Q-and-A below for a glimpse of what’s in store at the PAW Manufacturing conference. Q: What are the challenges in translating the lessons of predictive analytics from other

Boosting Performance of Machine Learning Models

  People often get stuck when they are asked to improve the performance of existing predictive models. What usually they do is try different algorithms and check their results. But often they end up not improving the...

Wise Practitioner – Predictive Analytics Interview Series: Tanay Chowdhury at Zurich North America

 In anticipation of his upcoming conference presentation, Deep Learning in Cloud Based Applications at Predictive Analytics World Chicago, June 20-23, 2016, we asked Tanay Chowdhury, Associate Data Scientist at Zurich North America, a few questions about his...

Feature Engineering within the Predictive Analytics Process — Part One

 What is Feature Engineering One of the growing discussions and debates within the data science community is the determination of inputs or variables that should be included in any predictive analytics algorithm. This type of process is...

The Executive’s Guide to Employee Attrition

 Much has been written about customer churn – predicting who, when, and why customers will stop buying, and how (or whether) to intervene. Employee churn is quite similar. Businesses want to predict who, when, and why employees...

Wise Practitioner – Predictive Analytics Interview Series: Lawrence Cowan at Cicero Group

 In anticipation of his upcoming conference presentation, Data Driven Selling: Enabling a Direct Salesforce with Tools that Re-Enforce Predictive Selling Methods at Predictive Analytics World Chicago, June 20-23, 2016, we asked Lawrence Cowan, Partner at Cicero Group,...

Wise Practitioner – Text Analytics Interview Series: John Herzer and Pengchu Zhang at Sandia National Laboratories

 In anticipation of their upcoming conference co-presentation, Enhancing search results relevance using Word2Vec Language Models at Text Analytics World Chicago, June 21-22, 2016, we asked Pengchu Zhang, Computer Scientist at Sandia National Laboratories, and John Herzer, Enterprise...

Wise Practitioner – Text Analytics Interview Series: Emrah Budur at Garanti Technology

 In anticipation of his upcoming conference presentation, Tips and Tricks on Developing High-performance Fuzzy Name Search Engine to Prevent Terrorism Financing at Text Analytics World Chicago, June 21-22, 2016, we asked Emrah Budur, Senior Software Engineer at...

Wise Practitioner – Predictive Analytics Interview Series: Thomas Schleicher at National Consumer Panel

 In anticipation of his upcoming conference presentation, Using Predictive Analytics to Optimize Organizational KPI’s: A Panel Market Research Case Study at Predictive Analytics World Chicago, June 20-23, 2016, we asked Thomas Schleicher, Sr. Director, Measurement Science at...

Ghosts in the Data, Constructing Data Entities

 Data Entities are seldom discussed concepts that primarily hide in the shadows or are ghosts on the periphery. These entities are data constructs that are observationally defined in terms of the underlying data set that can serve...

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