Interpretable Machine Learning

16 benchmarks537 papers

The goal of Interpretable Machine Learning is to allow oversight and understanding of machine-learned decisions. Much of the work in Interpretable Machine Learning has come in the form of devising methods to better explain the predictions of machine learning models.

<span class="description-source">Source: Assessing the Local Interpretability of Machine Learning Models </span>

Benchmarks

Interpretable Machine Learning on IMDb Movie Reviews

Interpretable Machine Learning on ValNov Subtask A

JOINT-F1 VAL-F1 NOV-F1

Interpretable Machine Learning on RR

Interpretable Machine Learning on ValNov Subtask B

JOINT-F1 VAL-F1 NOV-F1

Interpretable Machine Learning on CUB-200-2011

Interpretable Machine Learning on 10 Synthetic Genomics Datasets

1 Image, 2*2 Stitching, Exact Accuracy

Interpretable Machine Learning on CDCP

Interpretable Machine Learning on IAM Dataset

Interpretable Machine Learning on TACO -- Twitter Arguments from COnversations

Interpretable Machine Learning on TUD