Feature Importance
244 papers with code • 6 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Feature Importance
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Libraries
Use these libraries to find Feature Importance models and implementations
Digits
iris
Wine
Hotel Sales
simply-CLEVR
Latest papers
Application of the representative measure approach to assess the reliability of decision trees in dealing with unseen vehicle collision data
Machine learning algorithms are fundamental components of novel data-informed Artificial Intelligence architecture.
Automated discovery of symbolic laws governing skill acquisition from naturally occurring data
Skill acquisition is a key area of research in cognitive psychology as it encompasses multiple psychological processes.
Allowing humans to interactively guide machines where to look does not always improve human-AI team's classification accuracy
anguyen8/chm-corr-interactive
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We build CHM-Corr++, an interactive interface for CHM-Corr, enabling users to edit the feature attribution map provided by CHM-Corr and observe updated model decisions.
Interpretable Machine Learning for Survival Analysis
With the spread and rapid advancement of black box machine learning models, the field of interpretable machine learning (IML) or explainable artificial intelligence (XAI) has become increasingly important over the last decade.
Machine Learning Reveals Large-scale Impact of Posidonia Oceanica on Mediterranean Sea Water
Posidonia oceanica is a protected endemic seagrass of Mediterranean sea that fosters biodiversity, stores carbon, releases oxygen, and provides habitat to numerous sea organisms.
Explainable Global Wildfire Prediction Models using Graph Neural Networks
Wildfire prediction has become increasingly crucial due to the escalating impacts of climate change.
DimVis: Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting Machine
This paper presents DimVis, a visualization tool that employs supervised Explainable Boosting Machine (EBM) models (trained on user-selected data of interest) as an interpretation assistant for DR projections.
Efficient and Interpretable Traffic Destination Prediction using Explainable Boosting Machines
Developing accurate models for traffic trajectory predictions is crucial for achieving fully autonomous driving.
Classification of cotton water stress using convolutional neural networks and UAV-based RGB imagery
These findings highlighted the state-of-the-art performance of the proposed system in cotton water stress classification and provided valuable insights into the key image features contributing to accurate classification.
Dual feature-based and example-based explanation methods
Kozlov992/Dual-Explanation
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A new approach to the local and global explanation is proposed.
