Pages that link to "Item:Q5218493"

The following pages link to Definitions, methods, and applications in interpretable machine learning (Q5218493):

Displaying 43 items.

• Editable machine learning models? A rule-based framework for user studies of explainability (Q2022486) (← links)
• Beneficial and harmful explanatory machine learning (Q2051274) (← links)
• A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds (Q2057739) (← links)
• Default risk prediction and feature extraction using a penalized deep neural network (Q2080353) (← links)
• Structure-preserving neural networks (Q2127014) (← links)
• Physically interpretable machine learning algorithm on multidimensional non-linear fields (Q2128352) (← links)
• Scrutinizing XAI using linear ground-truth data with suppressor variables (Q2163233) (← links)
• High-resolution Bayesian mapping of landslide hazard with unobserved trigger event (Q2170422) (← links)
• Global sensitivity analysis in epidemiological modeling (Q2171531) (← links)
• Grouped feature importance and combined features effect plot (Q2172623) (← links)
• Conclusive local interpretation rules for random forests (Q2172632) (← links)
• SIRUS: stable and interpretable RUle set for classification (Q2219234) (← links)
• Making sense of raw input (Q2238697) (← links)
• Model transparency and interpretability: survey and application to the insurance industry (Q2677927) (← links)
• Flexible tree-structured regression models for discrete event times (Q2680302) (← links)
• A machine learning approach to differentiate between COVID-19 and influenza infection using synthetic infection and immune response data (Q2686729) (← links)
• Efficient Learning of Interpretable Classification Rules (Q5043563) (← links)
• Prediction, Estimation, and Attribution (Q5130603) (← links)
• A Survey on the Explainability of Supervised Machine Learning (Q5145841) (← links)
• Veridical data science (Q5854813) (← links)
• What are the Most Important Statistical Ideas of the Past 50 Years? (Q5881991) (← links)
• Local interpretation of supervised learning models based on high dimensional model representation (Q6057385) (← links)
• Prediction, Estimation, and Attribution (Q6068035) (← links)
• Data‐driven research in retail operations—A review (Q6072168) (← links)
• Stable Discovery of Interpretable Subgroups via Calibration in Causal Studies (Q6089483) (← links)
• Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressions (Q6125484) (← links)
• Visualizing the Implicit Model Selection Tradeoff (Q6135962) (← links)
• How to find a good explanation for clustering? (Q6136087) (← links)
• A reluctant additive model framework for interpretable nonlinear individualized treatment rules (Q6138648) (← links)
• Assessing the communication gap between AI models and healthcare professionals: explainability, utility and trust in AI-driven clinical decision-making (Q6156851) (← links)
• Cross-study replicability in cluster analysis (Q6166879) (← links)
• Explaining classifiers with measures of statistical association (Q6168907) (← links)
• Understanding the effect of contextual factors and decision making on team performance in Twenty20 cricket: an interpretable machine learning approach (Q6170882) (← links)
• Interpreting machine-learning models in transformed feature space with an application to remote-sensing classification (Q6176236) (← links)
• Total effects with constrained features (Q6547752) (← links)
• SOAR: simultaneous or-of-and rules for classification of positive and negative classes (Q6548823) (← links)
• A simple approach for local and global variable importance in nonlinear regression models (Q6561260) (← links)
• Rejoinder to: ``Machine learning applications in non-life insurance'' (Q6578119) (← links)
• Discussion of: ``Specifying prior distributions in reliability applications'': towards new formal rules for informative prior elicitation? (Q6581566) (← links)
• Transparency in Structural Research (Q6626345) (← links)
• Variable importance evaluation with personalized odds ratio for machine learning model interpretability with applications to electronic health records-based mortality prediction (Q6629964) (← links)
• On the efficient implementation of classification rule learning (Q6661121) (← links)
• A physical-information-flow-constrained temporal graph neural network-based simulator for granular materials (Q6669023) (← links)