Quantification of differences between feature importance rankings in Machine Learning

Quantifying differences in feature importance rankings of #machinelearning #classification could enhance #interpretability and #explainability: we show how through the rank-biased overlap similarity measure. Take a look at my novel work!

https://link.springer.com/chapter/10.1007/978-3-031-15037-1_11

Check also my oral communication at the Brain Informatics 2022

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s