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Found 14 papers, 11 papers with code
A Guide to Feature Importance Methods for Scientific Inference
Understanding the DGP requires insights into feature-target associations, which many ML models cannot directly provide, due to their opaque internal mechanisms.
Toward Understanding the Disagreement Problem in Neural Network Feature Attribution
In recent years, neural networks have demonstrated their remarkable ability to discern intricate patterns and relationships from raw data.
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.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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arfpy: A python package for density estimation and generative modeling with adversarial random forests
This paper introduces $\textit{arfpy}$, a python implementation of Adversarial Random Forests (ARF) (Watson et al., 2023), which is a lightweight procedure for synthesizing new data that resembles some given data.
survex: an R package for explaining machine learning survival models
Due to their flexibility and superior performance, machine learning models frequently complement and outperform traditional statistical survival models.
Interpreting Deep Neural Networks with the Package innsight
The R package innsight offers a general toolbox for revealing variable-wise interpretations of deep neural networks' predictions with so-called feature attribution methods.
Conditional Feature Importance for Mixed Data
The CPI enables conditional FI measurement that controls for any feature dependencies by sampling valid knockoffs - hence, generating synthetic data with similar statistical properties - for the data to be analyzed.
Unifying local and global model explanations by functional decomposition of low dimensional structures
The interventional SHAP value of feature $k$ is a weighted sum of the main component and all interaction components that include $k$, with the weights given by the reciprocal of the component's dimension.
Adversarial random forests for density estimation and generative modeling
We propose methods for density estimation and data synthesis using a novel form of unsupervised random forests.
Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process
Scientists and practitioners increasingly rely on machine learning to model data and draw conclusions.
Generalization of the Change of Variables Formula with Applications to Residual Flows
Normalizing flows leverage the Change of Variables Formula (CVF) to define flexible density models.
Testing Conditional Independence in Supervised Learning Algorithms
We propose the conditional predictive impact (CPI), a consistent and unbiased estimator of the association between one or several features and a given outcome, conditional on a reduced feature set.
Unbiased split variable selection for random survival forests using maximally selected rank statistics
However, instead of the conditional Monte-Carlo approach used in conditional inference forests, p-value approximations are employed.
ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
We introduce the C++ application and R package ranger.
