Slides from my m-cubed talk about Explaining complex machine learning models with LIME

[This article was first published on Shirin's playgRound, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

The last two days, I was in London for the M-cubed conference.

Here are the slides from my talk about Explaining complex machine learning models with LIME:

Traditional machine learning workflows focus heavily on model training and optimization; the best model is usually chosen via performance measures like accuracy or error and we tend to assume that a model is good enough for deployment if it passes certain thresholds of these performance criteria. Why a model makes the predictions it makes, however, is generally neglected. But being able to understand and interpret such models can be immensely important for improving model quality, increasing trust and transparency and for reducing bias. Because complex machine learning models are essentially black boxes and too complicated to understand, we need to use approximations.


I also took sketchnotes from the two keynote lectures:

HUMAN MINDS AND MACHINE INTELLIGENCE – WHO’S THE MASTER? by Dr Joanna Bryson

HUMAN MINDS AND MACHINE INTELLIGENCE – WHO’S THE MASTER? by Dr Joanna Bryson

A QUESTION OF MACHINE INTELLIGENCE – STRONG ARGUMENTS IN SUPPORT OF AND AGAINST DIFFERENT DEFINITIONS by Prof. Dagmar Monett Diaz

A QUESTION OF MACHINE INTELLIGENCE – STRONG ARGUMENTS IN SUPPORT OF AND AGAINST DIFFERENT DEFINITIONS by Prof. Dagmar Monett Diaz

To leave a comment for the author, please follow the link and comment on their blog: Shirin's playgRound.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)