predictive models
Originally published in Big Think When you harness the power and potential of machine learning, there are also some drastic downsides that you’ve got to manage. Deploying machine learning, you face the risk that it be discriminatory, biased, inequitable, exploitative, or opaque. In this article, I cover six ways that machine learning threatens social justice
By: Kalyan Veeramachaneni, Principal Research Scientist, Laboratory for Information and Decision systems (LIDS), MIT
Originally published at hbr.org
Businesses today are constantly generating enormous amounts of data, but that doesn’t always translate to actionable information. Over the past several years, my research group at MIT and I have sought answers to a fundamental question: What would...
In some of the more recent literature, discussion has ensued about the use of pure random or noise variables that end up as key variables in predictive models. In our big data environment with millions of records...
Research shows that people tend to be overly risk averse when weighing the potential success or failure of a decision. This tendency is compounded when we consider the vast number of decisions being made across an organization....
Do you talk to your computer or smartphone? Just a few years ago, that question would have been absurd. But with advances in natural language processing, the likelihood is that you have asked your phone to send...
Enterprises are inundated with data from social, mobile, IoT and other technologies. The pace of the data flow is only accelerating. Over 300 hours of videos are uploaded on YouTube every minute, and this has grown from...
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?”...
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: Thomas H. Davenport
In anticipation of the forthcoming Revised and Updated, paperback edition of Eric Siegel’s Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (coming January 6, 2016—preorder today), read here its Foreword by Thomas...
Predictive analytics is often cited as a key business driver for Big Data. It is easy to see how predictive analytics, when done right, can represent a strong competitive advantage for companies. Such as: Enabling companies to...
