arXiv research

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 ranks and selects features without model fitting.

SAGE quantifies feature importance in machine learning models.

DEDACT breaks down feature importance into direct and associative components.

A new method uses asymmetric Shapley values to assess gene importance in clinical prediction models.

A novel approach combines feature importance scores with deep learning for forex price prediction.

Have you ever wondered how your feature space is impacting the prediction of a specific sample in your dataset? In this paper, we introduce Single Sample Feature Importance (SSFI), which is an interpretable feature importance algorithm that allows for the identification of the most important features that contribute to…

Paper uses Shapley values to explain how clusters of training data affect predictions.

Study predicts academic achievement using students' support networks.

Proposes a framework to assess feature importance without algorithm constraints.

Complex problems may require sophisticated, non-linear learning methods such as kernel machines or deep neural networks to achieve state of the art prediction accuracies. However, high prediction accuracies are not the only objective to consider when solving problems using machine learning. Instead, particular scientif…

Given a model $f$ that predicts a target $y$ from a vector of input features $\pmb{x} = x_1, x_2, \ldots, x_M$, we seek to measure the importance of each feature with respect to the model's ability to make a good prediction. To this end, we consider how (on average) some measure of goodness or badness of prediction (wh…

New model improves prediction accuracy and interpretability for network-connected data.

Proposes a method to quantify uncertainty in predictions under covariate shift.

The paper investigates causal relationships in heart failure prediction using machine learning.

Combining feature importance estimates improves reliability of machine learning predictions.

Researchers develop a new method to assess variable importance in spatial machine learning models for air pollution exposure prediction.

DFI maps covariates to latent representations for feature importance.

New FI method accurately predicts feature importance.

For nonlinear supervised learning models, assessing the importance of predictor variables or their interactions is not straightforward because it can vary in the domain of the variables. Importance can be assessed locally with sensitivity analysis using general methods that rely on the model's predictions or their deri…

The paper emphasizes the importance of joint predictions over marginal predictions for decision-making.

Efficiently estimates variable importance in prediction tasks using Shapley values.

In developing countries like India agriculture plays an extremely important role in the lives of the population. In India, around 80\% of the population depend on agriculture or its by-products as the primary means for employment. Given large population dependency on agriculture, it becomes extremely important for the …

Hierarchical-CPI improves variable importance measurement for medical data.

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…

New method identifies important features and interactions in RF models.

Local MDI+ improves feature importance for tree-based models, enhancing interpretability and performance.

New method corrects bias in feature importance measures of GBM.

A new method for variable importance measures without impossible data.

Develops method to assess feature importance in black-box models for unconditional distribution.

In this work, we investigate the importance of ethnicity in colorectal cancer survivability prediction using machine learning techniques and the SEER cancer incidence database. We compare model performances for 2-year survivability prediction and feature importance rankings between Hispanic, White, and mixed patient po…

Link prediction is an important way to complete knowledge graphs (KGs), while embedding-based methods, effective for link prediction in KGs, perform poorly on relations that only have a few associative triples. In this work, we propose a Meta Relational Learning (MetaR) framework to do the common but challenging few-sh…

Algorithm calibrates predictions for covariate shift using domain adaptation.

Paper proposes a new method for feature importance in model selection.

New method disentangles feature importance scores in machine learning.

The paper extends explainability methods to uncertainty-aware models, revealing feature impacts on predictive entropy and likelihood.

Introduces joint Shapley values to measure feature importance in models.

RbX generates local explanations for black-box models using greedy polytope construction.

Proposes new methods for interpreting document classification models.

This paper deals with prediction of anopheles number, the main vector of malaria risk, using environmental and climate variables. The variables selection is based on an automatic machine learning method using regression trees, and random forests combined with stratified two levels cross validation. The minimum threshol…

Importance sampling (IS) is a common reweighting strategy for off-policy prediction in reinforcement learning. While it is consistent and unbiased, it can result in high variance updates to the weights for the value function. In this work, we explore a resampling strategy as an alternative to reweighting. We propose Im…

Study examines challenges in variable importance ranking due to feature correlation.

Electronic health records are an increasingly important resource for understanding the interactions between patient health, environment, and clinical decisions. In this paper we report an empirical study of predictive modeling of several patient outcomes using three state-of-the-art machine learning methods. Our primar…

FIT evaluates time series model feature importance quantifying distributional shift.

Study uses deep learning to predict mycotoxin levels in Irish oats.

This thesis improves protein contact prediction using unsupervised and supervised methods.

Global Sensitivity Analysis improves feature importance ranking in Random Forests.

SCORE improves tree-based predictions with boosted residual extraTrees.