Paper exposes vulnerabilities in interpreting machine learning models using adversarial attacks on PD plots.
problem Vulnerability of permutation-based interpretation methods, particularly PD plots, to adversarial attacks.
method Adversarial framework to manipulate black-box models and produce deceptive PD plots.
result It is possible to hide discriminatory behaviors in machine learning models through interpretation tools like PD plots.
Unified framework for interpreting complex regression models with many predictors.
problem Interpreting nonparametric regression models with many predictors.
method Derivative-based approach for existing tools like partial-dependence plots.
result New technique called accumulated total derivative effects plot for complex models.
SHAP explains boosted trees with additively modeled features.
problem Explaining predictions of boosted trees models with additively modeled features.
method SHAP values for additively modeled features in boosted trees models.
result SHAP dependence plot matches partial dependence plot for additively modeled features.
Automates selection and visualization of model responses in various directions.
problem Manual selection and limited visualization of PDPs.
method Formalizes method for automating PDP selection and extends to arbitrary directions.
result Demonstrates usefulness across model selection, bias detection, and latent space exploration.
Paper quantifies how much machine learning models can be explained.
problem Quantifying the explainability of machine learning models.
method Developed a framework to quantify explainability of arbitrary machine learning models.
result Allows for judgment on sufficiency of model explanations.
Researchers show how to manipulate Partial Dependence plots to deceive explanations of predictive models.
problem The robustness and trustworthiness of Partial Dependence (PD) explanations are compromised.
method Data poisoning using genetic and gradient algorithms to manipulate PD plots.
result PD explanations can be fooled and manipulated to mislead understanding of predictive models.
Researchers formalize PD and PFI to relate them to data generating process.
problem Lack of theory linking PD and PFI to data generating process.
method Formalize PD and PFI as estimators of ground truth estimands, account for model variance with learner-PD and learner-PFI.
result PD and PFI estimates deviate from ground truth due to statistical biases, model variance, and Monte Carlo approximation errors.
A new method detects interactions in machine learning models.
problem Interpreting non-linear and interaction effects in machine learning models.
method Regional effect plots with implicit interaction detection.
result The method quantifies and interprets feature effects reliably, less confounded by interactions.
We use PDPs with confidence bands to explain HPO results.
problem Lack of explainable insights into HPO results.
method Use IML techniques, specifically PDPs, with estimated confidence bands.
result Increased quality of PDPs in relevant sub-regions.
Study robustness of global feature effect explanations in machine learning models.
problem Vulnerability of global feature effect explanations to data and model perturbations.
method Theoretical bounds and experimental evaluation of partial dependence plots and accumulated local effects.
result Quantifies the gap between best and worst-case scenarios of misinterpreting machine learning predictions globally.
New method quantifies model complexity for better interpretation.
problem Complex models produce misleading interpretation results.
method Functional decomposition to quantify model complexity.
result Post-hoc interpretation of complex models is more reliable and compact.
Paper uses machine learning to model travel mode switching under a new transit system.
problem Modeling individual travel mode preferences and response to new mobility options.
method Interpretable machine learning approach to predict and interpret mode-switching behavior.
result Machine learning captures individual heterogeneity in travel mode choice.
New methods needed for accurate feature importance due to feature dependencies.
problem Misleading variable importance measures from PaP methods due to feature dependencies.
method Alternative approaches involving additional modeling to avoid extrapolation.
result PaP metrics can over-emphasize correlated features, requiring more direct methods.
Model-agnostic interpretation methods can mislead if not used carefully.
problem Misinterpretation of machine learning models due to improper use of techniques.
method General pitfalls of model-agnostic interpretation methods.
result Many pitfalls exist when using global interpretation techniques for machine learning models.
The abstract discusses various machine learning explanation methods.
problem Improving the understanding and trust in machine learning models.
method Exploratory methods for assessing machine learning models.
result Various methods exist, each with its own strengths and applications.
A new tree-based estimator, FastPD, efficiently estimates PD functions for machine learning models.
problem Efficiently estimating Partial Dependence functions for machine learning models.
method Proposes a new tree-based estimator, FastPD, to estimate PD functions.
result FastPD consistently estimates the desired population quantity and improves complexity from quadratic to linear.
AI techniques explain synthetic tabular data weaknesses.
problem Challenges in evaluating synthetic tabular data quality.
method Apply explainable AI to a binary detection classifier.
result Reveals inconsistencies, unrealistic dependencies, or missing patterns in synthetic data.
New approach interprets machine learning models through feature space transformations.
problem Interpreting models with strongly dependent features in high-dimensional spaces.
method Feature space transformations, including PCA and partial orthogonalization.
result Enhances model interpretation tools for domain experts.
Proposes a new method for interpreting feature importance and effects in dependent feature models.
problem Challenges in interpreting feature importance when features are dependent and interactions are present.
method Conditional Subgroup Approach
result Conditional PFI and PDP estimates based on this approach often outperform existing methods.
Unified local and global explanations through functional decomposition of low dimensional structures.
problem Tackles the challenge of extracting meaningful local and global explanations from machine learning models.
method Proposes a new identification constraint to decompose the global representation into main and interaction components of arbitrary order.
result Unified local and global explanations by connecting partial dependence plots and interventional SHAP values.
We propose a novel methodology, forest floor, to visualize and interpret random forest (RF) models. RF is a popular and useful tool for non-linear multi-variate classification and regression, which yields a good trade-off between robustness (low variance) and adaptiveness (low bias). Direct interpretation of a RF model…
New method improves HPO interpretability without sacrificing performance.
problem Difficulty in understanding HPO algorithms due to black-box nature.
method Coupling Bayesian optimization with Bayesian Algorithm Execution.
result More reliable IML explanations without compromising optimization performance.
GADGET framework decomposes global feature effects using recursive partitioning.
problem Misleading global feature effects when feature interactions are present.
method Generalized additive decomposition of global effects (GADGET) based on recursive partitioning.
result Minimizes interaction-related heterogeneity of local feature effects.
Proposes ICE-based metric for better understanding interactions in black-box models.
problem Misleading global sensitivity metrics in black-box models due to interaction effects.
method Individual Conditional Expectation (ICE) curves to compute feature importance and interactions.
result ICE-based metric provides richer insights into feature importance and interactions.
Study compares imputation methods' effects on IML confidence intervals.
problem Missing data impacts IML interpretation and confidence intervals.
method Compared single vs multiple imputation methods on IML confidence intervals.
result Multiple imputation provides closer coverage to nominal than single imputation.
In recent years, a large amount of model-agnostic methods to improve the transparency, trustability and interpretability of machine learning models have been developed. We introduce local feature importance as a local version of a recent model-agnostic global feature importance method. Based on local feature importance…
Hollow-tree Super resolves feature importance in large datasets.
problem Lack of effective scaling for large feature numbers in boosted tree models.
method Hollow-tree Super (HOTS) methodology for feature importance visualization.
result HOTS effectively resolves feature importance and directionality in high-dimensional neuroscientific data.
Study examines how balancing methods affect model behavior in imbalanced classification problems.
problem Impact of balancing methods on model behavior in imbalanced classification problems.
method Used Explainable Artificial Intelligence tools (variable importance method, partial dependence profile, accumulated local effects) to compare model behavior before and after balancing.
result Significant changes in model behavior due to balancing methods, leading to biased models.
New methods compute partial dependence curves directly from data.
problem Limited accessibility and interpretability of partial dependence curves.
method Developed methods for numerical and categorical variables, directly from training data.
result Our methods provide a direct estimate of partial dependence without fitting models.
The paper extends explainability methods to uncertainty-aware models, revealing feature impacts on predictive entropy and likelihood.
problem Understanding the factors contributing to uncertainty in probabilistic models.
method Adapting permutation feature importance, partial dependence plots, and individual conditional expectation plots to measure feature impacts on predictive entropy and likelihood.
result Novel insights into model behaviour and feature impacts on uncertainty are obtained.
New tools explain FRF model predictions in high-dimensional ECG data.
problem Lack of interpretability in Functional Random Forests (FRF) models.
method Introduces FPDPs, FPC Probability Heatmaps, and various FPC importance metrics.
result Enhances transparency of FRF models by revealing FPC contributions.
A neural network approach for efficient conditional SHAP calculations.
problem Efficiently calculating conditional SHAP values for various models.
method Surrogate neural network approach for conditional SHAP.
result Efficiently calculates conditional SHAP values for neural networks and other regression models.
The paper proposes a method to improve model calibration without explicitly measuring uncertainties.
problem Improving the quality of uncertainty estimators in deep neural networks.
method A novel algorithm that performs simultaneous interval estimation for different calibration levels, effectively refining mean estimates.
result The proposed approach consistently outperforms existing regularization strategies in deep regression models.
Analyzes error sources in global feature effect estimation methods.
problem Unexplored error sources in global feature effect estimation methods.
method Systematic, estimator-level analysis of bias and variance.
result Holdout data is theoretically cleanest, but estimation variance depends on sample size and model characteristics.
LLMs simulate financial markets, revealing consistent trading strategies and market dynamics.
problem Testing financial theories with AI trading agents.
method Simulated stock market with LLMs using a persistent order book and varied strategies.
result LLMs can simulate different trading strategies and market dynamics.
Interpreting predictions from tree ensemble methods such as gradient boosting machines and random forests is important, yet feature attribution for trees is often heuristic and not individualized for each prediction. Here we show that popular feature attribution methods are inconsistent, meaning they can lower a featur…
Automate residual plot assessment with R package and Shiny application
problem Diagnosing linear models
method Computer vision model for residual plot assessment
result Predicts visual signal strength and supports model fit assessment
This research optimizes Andrews plots for better visual clarity in high-dimensional data.
problem Visualizing high-dimensional datasets with clarity and aesthetics.
method Developed a method to add spectral smoothing to Andrews plots to reduce visual clutter.
result Optimal spatial-spectral smoothing leads to more aesthetically pleasing and clutter-free visualizations.
SMT-EX enhances SMT for explaining surrogate models of mixed-variable design problems.
problem Making decisions and understanding complex systems using surrogate models of mixed-variable design problems.
method Integrates explainability techniques into SMT, including Shapley Additive Explanations, Partial Dependence Plot, and Individual Conditional Expectations.
result Demonstrates versatility in addressing diverse problem characteristics.
3D filament plots visualize curves in datasets, avoiding visual clutter.
problem Visualizing high-dimensional data relationships in scatterplots of points.
method Construct 3D filament plots using linear isometries and Frenet-Serret systems.
result 3D filament plots preserve Euclidean distances and avoid visual clutter.
A new visualization tool MD plot discovers interesting structures in continuous features.
problem Identifying interesting structures in data distributions, especially with skewed, clipped, or multimodal distributions.
method Proposes a new visualization tool called the mirrored density plot (MD plot) that does not require adjusting density estimation parameters.
result The MD plot outperforms conventional methods in identifying structures in complex distributions.
Used to investigate the presence of distinctive recurrent behaviours in natural processes, the recurrence plots can be applied to the analysis of economic data, and, in particular, to the characterization of exchange rates of currencies too. In this paper, we will show that these plots are able to characterize the peri…
Pareto distributions, and power laws in general, have demonstrated to be very useful models to describe very different phenomena, from physics to finance. In recent years, the econophysical literature has proposed a large amount of papers and models justifying the presence of power laws in economic data. Most of the ti…
Introduces PIT-plot for prioritizing projects based on their impact.
problem Optimizing R&D investments in project portfolios.
method Develops a new tool (PIT-plot) focusing on project impact rather than project properties.
result Identifies projects with the largest impact for risk mitigation or value-adding.
Charts are an excellent way to convey patterns and trends in data, but they do not facilitate further modeling of the data or close inspection of individual data points. We present a fully automated system for extracting the numerical values of data points from images of scatter plots. We use deep learning techniques t…
InterpretML simplifies machine learning interpretability for users and researchers.
problem Making machine learning models understandable to non-experts.
method Unified Python package exposing interpretability algorithms and visualization.
result First implementation of Explainable Boosting Machine, a powerful, interpretable model.
PLOT uses optimal transport to find neural site handles for causal abstraction.
problem Finding the relevant neural site for causal analysis is computationally challenging.
method PLOT employs optimal transport to localize causal variables from neural network outputs.
result PLOT efficiently finds intervention handles for causal abstraction in neural networks.
Develops Austen plots for assessing bias from unobserved confounding in observational studies.
problem Bias in causal estimates due to unobserved confounding.
method Formalizes confounding strength, uses Austen plots to visualize and quantify bias.
result Allows domain experts to assess the plausibility of strong confounders.