Description:

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.

Prerequisites:

A basic understanding of ML models

Speaker Info:

Anand is a co-founder of Gramener, a data science company. He leads a team of data enthusiasts with skills in analysis, design, programming and statistics.

Speaker Links:

• Videos of previous talks
• Slides of previous talks