Machine learning algorithms are increasingly black-box models. However, their outputs are business data that humans need to understand and act upon. For example, if a clustering model suggests 4 customer clusters, how do we identify and characterize these? If a random forest model suggests a pattern of classification, how do we understand the dominant factors and the irrelevant ones? These topics fall under the umbrella of model visualization — where the inputs, process, and output of machine learning models are the topic of understanding. This talk explores some of the prevalent ways of visualizing machine learning models.
ABOUT SPEAKER
Anand is founder and CEO of Gramener, a leading Data Science company.Anand provides the technology leadership and strategic direction to the organization. Anand and his team explore insights from data and communicate these as visual stories. He has an MBA from IIM Bangalore and a B.Tech from IIT Madras.
He has worked at IBM, Lehman Brothers, The Boston Consulting Group and Infosys Consulting. He blogs at S-ANAND.NET. Anand is a data geek and his affair with programmatically analyzing data began back in the mid-nineties when he was in college at IIT Madras. Taking off from there, Anand was drawn into the maze of data and his initial tools were Linux based grep, sed and awk. He then moved on to Shell scripts and PERL.
“Tools are never the concern, the motive and the execution is,” believes Anand