Tree ensemble methods such as random forests [Breiman, 2001] are very popular to handle high-dimensional tabular data sets, notably because of their good predictive accuracy. However, when machine learning is used for decision-making problems, settling for the best predictive procedures may not be reasonable since enli…
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48 results for “input variable importance”
A new method for variable importance measures without impossible data.
problem Using impossible data for variable importance measures in black box models.
method Cohort Shapley, a method grounded in economic game theory using only observed data.
result Cohort Shapley provides a more trustworthy explanation of black box models' decisions.
Package provides sensitivity analysis for neural networks.
problem Understanding how input variables affect neural network predictions.
method Partial derivatives method to calculate sensitivities.
result Allows evaluation of variable importance and input-output relationships.
New method quantifies variable importance in causal forests for treatment effect heterogeneity.
problem Lack of understanding how input variables affect treatment effect heterogeneity in causal forests.
method Developed a new importance variable algorithm for causal forests based on the drop and relearn principle.
result Shows how to handle forest retraining without a confounding variable and introduces a corrective term for confounders.
MultiImport infers node importance from multiple KG signals.
problem Inferring node importance in a knowledge graph from multiple input signals.
method End-to-end latent variable model using attentive graph neural networks.
result MultiImport consistently outperforms existing methods, achieving up to 23.7% higher NDCG@100.
Natural Language Processing models help encode categorical process inputs.
problem Encoding categorical variables in industrial process modeling.
method Using NLP models for categorical variable encoding, combined with dimensionality reduction.
result Meaningful embeddings of categorical variables improve feature importance.
A new variable importance measure for DRFs detects broader impacts on output distributions.
problem Estimating full conditional distributions of multivariate outputs given inputs.
method Based on the drop and relearn principle and MMD distance.
result Consistent and high-performing variable importance measure for DRFs.
SHAFF estimates Shapley effects efficiently even with dependent variables.
problem Challenges in estimating Shapley effects, especially with dependent variables.
method SHAFF uses random forests to estimate Shapley effects efficiently.
result SHAFF provides a fast and accurate estimate of Shapley effects.
A neural network finds causal relationships among latent variables.
problem Learning causal structure among latent variables in high-dimensional data.
method Redundant Input Neural Network (RINN) with modified architecture and regularized objective function.
result The RINN method successfully recovers latent causal structure between input and output variables.
IANN visualizes all input variables effects simultaneously.
problem Inability to visualize all input variables effects simultaneously in black-box functions.
method Interpretable Architecture Neural Network (IANN) approach.
result Visualization of all input variables effects directly and simultaneously.
Prediction and explanation are key objects in supervised machine learning, where predictive models are known as black boxes and explanatory models are known as glass boxes. Explanation provides the necessary and sufficient information to interpret the model output in terms of the model input. It includes assessments of…
From global to local MDI variable importances for random forests and when they are Shapley valuesstat.ML
New local MDI variable importances derived from global scores match Shapley values.
problem Local feature relevance in tree-based models.
method Deriving local MDI importance measure from global scores and linking it to Shapley values.
result Local MDI importances have a natural connection with Shapley values.
Defines a new metric to measure importance of predictors in complex machine learning models.
problem Measuring importance of predictors in black box machine learning models.
method Introduces a new metric, GVIM, based on true conditional expectation functions and causal interpretation.
result The GVIM can be represented as a function of Conditional Average Treatment Effect (CATE), providing a causal interpretation.
STAM learns important time steps and variables for multivariate time series prediction.
problem Accurate interpretation of multivariate time series predictions.
method Spatiotemporal attention mechanism (STAM) for multivariate time series modeling.
result STAM maintains state-of-the-art prediction accuracy with improved interpretability.
Two ANOVA-based algorithms boost random Fourier feature models for function approximation.
problem Approximating high-dimensional functions with low-order interactions.
method Utilizes ANOVA decomposition to learn low-order functions and index sets of important variables.
result Significantly reduces approximation error compared to existing methods.
Proposes VEESA pipeline for interpreting ML models with functional data.
problem Interpreting ML models with functional data inputs for high-consequence applications.
method VEESA pipeline using efPCA and PFI.
result Provides explanation of model's use of functional data variability for prediction.
Global Sensitivity Analysis for Engineering Design Based on Individual Conditional Expectationscs.LG
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.
We introduce a variable importance measure to quantify the impact of individual input variables to a black box function. Our measure is based on the Shapley value from cooperative game theory. Many measures of variable importance operate by changing some predictor values with others held fixed, potentially creating unl…
Fast method estimates variable importance for large neural networks.
problem Estimating variable importance in large neural networks is computationally expensive and lacks theoretical guarantees.
method Linearization initialized at full model parameters with ridge-like penalty.
result Estimates variable importance with error rate of and asymptotic normality.
New formulas for feature importance tests in regression models.
problem Identifying important features in regression models.
method Established formulas for AUC criteria and proposed alternative metrics.
result Integrated Gradients (IG) performs nearly as well as Kernel SHAP (KS) but is faster.
Enhancing Variable Importance in Random Forests: A Novel Application of Global Sensitivity Analysisstat.ML
Global Sensitivity Analysis improves feature importance ranking in Random Forests.
problem Improving feature importance ranking in Random Forests.
method Applying Global Sensitivity Analysis to Random Forests for feature ranking.
result Our method provides a novel way to rank features based on their importance.
We develop a method for quantile-based sensitivity analysis in models with discontinuities.
problem Uncertainty in interpreting discontinuous models using traditional derivatives.
method Quantile-based derivatives for discontinuous models with discrete inputs.
result Derivatives of quantile-based outputs are well-defined and provide meaningful insights.
A new model-free variable importance method (IGCS) is introduced for high-dimensional data.
problem Model-free variable importance for high-dimensional data, especially when prediction functions are proprietary or expensive.
method Integrated Gradient (IG) version of Cohort Shapley (CS) method with cost.
result IGCS closely matches Cohort Shapley (CS) in performance, especially for binary predictors.
In many cases, feature selection is often more complicated than identifying a single subset of input variables that would together explain the output. There may be interactions that depend on contextual information, i.e., variables that reveal to be relevant only in some specific circumstances. In this setting, the con…
In this paper we use wavelet concepts to show that correlation coefficient between two financial data's is not constant but varies with scale from high correlation value to strongly anti-correlation value This studies is important because correlation coefficient is used to quantify degree of independence between two va…
Field canals improvement projects (FCIPs) are one of the ambitious projects constructed to save fresh water. To finance this project, Conceptual cost models are important to accurately predict preliminary costs at the early stages of the project. The first step is to develop a conceptual cost model to identify key cost…
RS-HDMR-GPR simplifies complex functions with machine-learned lower-dimensional terms.
problem Representing and understanding complex multidimensional functions with sparse data.
method Random Sampling High Dimensional Model Representation Gaussian Process Regression (RS-HDMR-GPR).
result Facilitates recovery of functional dependence and adds insight into input variable importance.
XAI methods struggle with identifying true predictors from suppressors in linear datasets.
problem XAI methods misidentify suppressor variables as important features.
method Carefully crafted linear ground-truth dataset to study suppressor variables; evaluated various XAI methods.
result Most XAI methods fail to distinguish true predictors from suppressors in linear settings.
In modern data analysis, random sampling is an efficient and widely-used strategy to overcome the computational difficulties brought by large sample size. In previous studies, researchers conducted random sampling which is according to the input data but independent on the response variable, however the response variab…
Analyzing and utilizing spatiotemporal big data are essential for studies concerning climate change. However, such data are not fully integrated into climate models owing to limitations in statistical frameworks. Herein, we employ VARENN (visually augmented representation of environment for neural networks) to efficien…
This paper clarifies VAE's property through geometric and information-theoretic interpretations.
problem The transparency of VAE model is an underlying issue.
method Quantitative understanding of VAE through differential geometry and information theory.
result VAE can be mapped to an implicit isometric embedding with a scale factor derived from the posterior parameter.
We propose the Sobolev Independence Criterion (SIC), an interpretable dependency measure between a high dimensional random variable X and a response variable Y . SIC decomposes to the sum of feature importance scores and hence can be used for nonlinear feature selection. SIC can be seen as a gradient regularized Integr…
In this work, we propose a simple but effective method to interpret black-box machine learning models globally. That is, we use a compact binary tree, the interpretation tree, to explicitly represent the most important decision rules that are implicitly contained in the black-box machine learning models. This tree is l…
Framework estimates multiple plausible solutions with uncertainty measures.
problem Machine learning models need to propose multiple plausible solutions with meaningful uncertainty.
method Discrete latent variables model one-to-many mappings, allowing effective conditional probability estimation.
result Framework outperforms state-of-the-art in uncertainty estimation and is practical.
Deep neural networks (DNNs) are famous for their high prediction accuracy, but they are also known for their black-box nature and poor interpretability. We consider the problem of variable selection, that is, selecting the input variables that have significant predictive power on the output, in DNNs. We propose a backw…
Efficient optimisation of black-box problems that comprise both continuous and categorical inputs is important, yet poses significant challenges. We propose a new approach, Continuous and Categorical Bayesian Optimisation (CoCaBO), which combines the strengths of multi-armed bandits and Bayesian optimisation to select …
New method R-LOCO improves local feature importance analysis.
problem Local attribution methods fail to accurately identify important features.
method R-LOCO segments input space into regions and applies global methods within.
result R-LOCO delivers more accurate local attributions.
Variable importance is central to scientific studies, including the social sciences and causal inference, healthcare, and other domains. However, current notions of variable importance are often tied to a specific predictive model. This is problematic: what if there were multiple well-performing predictive models, and …
A new method improves feature importance and model stress-testing reliability.
problem Estimating feature contributions in machine learning models for trust and transparency.
method Replacing multiple random permutations with a single, deterministic, and optimal permutation.
result Improved bias-variance tradeoffs and accuracy in challenging scenarios.
CDPs visualize causal dependencies in AI models.
problem Understanding how AI models depend on data inputs causally.
method Developed Causal Dependence Plots (CDPs) to visualize causal dependencies.
result CDPs show causal changes in predictors and outcomes.
In latent Gaussian trees the pairwise correlation signs between the variables are intrinsically unrecoverable. Such information is vital since it completely determines the direction in which two variables are associated. In this work, we resort to information theoretical approaches to achieve two fundamental goals: Fir…
Interprets neural network classifiers for categorical inputs.
problem Neural networks' interpretability in human-sensitive applications.
method Mapping to physical energy model, expansion of neural network layers.
result Each layer's contribution to classification can be analyzed.
Shapley Flow interprets model predictions using a graph-based approach to feature importance.
problem Existing feature importance methods ignore or hide feature dependencies.
method Shapley Flow considers the entire causal graph and assigns credit to edges.
result Shapley Flow provides a deeper, graph-based view of feature importance.
XCM improves MTS classification with explainable deep learning.
problem Lack of explainable deep learning models for MTS classification.
method XCM is a compact CNN that extracts variable and timestamp information directly from input data.
result XCM outperforms state-of-the-art MTS classifiers on large and small datasets.
Random Forest variable importance is improved by class balancing techniques.
problem Class imbalance problem in machine learning.
method Proposed a variable selection algorithm using RF variable importance and its confidence interval.
result Our algorithm efficiently selects an optimal feature set, leading to improved prediction performance.
Random forests are a statistical learning method widely used in many areas of scientific research because of its ability to learn complex relationships between input and output variables and also its capacity to handle high-dimensional data. However, current random forest approaches are not flexible enough to handle he…
Electronic Medical Records (EMR) are a rich source of patient information, including measurements reflecting physiologic signs and administered therapies. Identifying which variables are useful in predicting clinical outcomes can be challenging. Advanced algorithms such as deep neural networks were designed to process …
Optimal kernel learning improves GP regression for high-dimensional inputs.
problem High computational costs and low prediction accuracy in GP models with many inputs.
method Approximates GP covariance with a convex combination of kernel functions, identifying active variables.
result Improves prediction accuracy and correctly identifies active input variables.