Feature Importance
6개 벤치마크 · 논문 1,170편 · 이 태스크의 논문 보기 →
Benchmarks
Most implemented
FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
A Unified Approach to Interpreting Model Predictions
RISE: Randomized Input Sampling for Explanation of Black-box Models
FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine
Attention is not Explanation
Interpretable machine learning: definitions, methods, and applications
Papers
A unified framework for global and local interpretability using adaptive derivative-ordered random explanation
The interpretability of complex machine learning models is of paramount importance, especially in real-world high-stakes domains such as healthcare and finance. However, existing post-hoc interpretability methods suffer …
Computational EfficiencyFeature ImportanceAn Explainable Machine Learning Framework for Predicting Blood-Brain Barrier Permeability Using Molecular Descriptors
Blood-brain barrier (BBB) permeability is a critical determinant in the development of central nervous system therapeutics because it directly influences the ability of drug candidates to reach their target sites within …
Hyperparameter OptimizationFeature ImportanceTiming-Aware Repurchase Prediction for Web-Scale E-Commerce: Survival Models for Multi-Surface Grocery Recommendation
Repurchase recommenders in e-commerce are commonly framed as a binary question asking "will this customer buy this item within W days", a formulation that requires a separately trained model for every horizon of interest…
Feature ImportanceActionable CBFI: Integrating Structural Decomposition and Causal Counterfactual Recourse for Tabular Machine Learning
Explainable artificial intelligence (XAI) increasingly calls for actionable counterfactual recourse, yet current methodologies face challenges related to causal invalidity, excessive cognitive burden, and predictive fail…
Feature ImportanceEnergy Yield and Lifetime Climate Classification via Machine Learning for Optimizing Photovoltaic Module Design and Materials
To resiliently and sustainably meet our future energy demand, photovoltaic (PV) modules must be deployed across a broad and diverse range of geographical regions with varying operating conditions. As these conditions str…
Feature ImportanceSAGE-XGBoost: Spatially Augmented Graph Embeddings--Machine Learning Framework for Natural Hazards Susceptibility Mapping under Data Scarcity
Natural hazard susceptibility mapping is often constrained by limited labeled data, reducing the generalizability of conventional machine learning and limiting the applicability of complex deep learning models. This stud…
Representation LearningFeature ImportanceData Augmentation