Looking beyond accuracy to improve trust in machine learning

January 9, 2018
By

(This article was first published on Shirin's playgRound, and kindly contributed to R-bloggers)

I have written another blogpost about Looking beyond accuracy to improve trust in machine learning at my company codecentric’s blog:

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 to get a better sense of how they work. One such approach is LIME, which stands for Local Interpretable Model-agnostic Explanations and is a tool that helps understand and explain the decisions made by complex machine learning models.

Continue reading at https://blog.codecentric.de/en/2018/01/look-beyond-accuracy-improve-trust-machine-learning/

Links to the entire example code and more info are given at the end of the blog post.

The blog post is also available in German.

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 on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers


Sponsors

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)