6 citations · 6 across the 4 of their papers we have counts for
5 papers
Aggregate Models, Not Explanations: Improving Feature Importance Estimation
Joseph Paillard, Angel Reyero Lobo, Denis A. Engemann +1
Feature-importance methods show promise in transforming machine learning models from predictive engines into tools for scientific discovery. However, due to data sampling and algor…
Hierarchical Variable Importance with Statistical Control for Medical Data-Based Prediction
Joseph Paillard, Antoine Collas, Denis A. Engemann +1
Recent advances in machine learning have greatly expanded the repertoire of predictive methods for medical imaging. However, the interpretability of complex models remains a challe…
Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence
Joseph Paillard, Angel Reyero Lobo, Vitaliy Kolodyazhniy +2
Causal machine learning holds promise for estimating individual treatment effects from complex data. For successful real-world applications of machine learning methods, it is of pa…
Variable Importance in High-Dimensional Settings Requires Grouping
Ahmad Chamma, Bertrand Thirion, Denis A. Engemann
Explaining the decision process of machine learning algorithms is nowadays crucial for both model's performance enhancement and human comprehension. This can be achieved by assessi…
Statistically Valid Variable Importance Assessment through Conditional Permutations
Ahmad Chamma, Denis A. Engemann, Bertrand Thirion
Variable importance assessment has become a crucial step in machine-learning applications when using complex learners, such as deep neural networks, on large-scale data. Removal-ba…