How to Explain Individual Classification Decisions

Details How to Explain Individual Classification Decisions How to Explain Individual Classification Decisions

2009-12-06

arxiv.org/abs/0912.1128v1

Statistics

Machine Learning

31 pages, 14 figures

Scientific paper

After building a classifier with modern tools of machine learning we typically have a black box at hand that is able to predict well for unseen data. Thus, we get an answer to the question what is the most likely label of a given unseen data point. However, most methods will provide no answer why the model predicted the particular label for a single instance and what features were most influential for that particular instance. The only method that is currently able to provide such explanations are decision trees. This paper proposes a procedure which (based on a set of assumptions) allows to explain the decisions of any classification method.

Affiliated with

Also associated with

No associations

Rating

How to Explain Individual Classification Decisions does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with How to Explain Individual Classification Decisions, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and How to Explain Individual Classification Decisions will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-84195