Tree ensembles such as Random Forests have achieved impressive empirical success across a wide variety of applications. To understand how these models make predictions, people routinely turn to feature importance measures calculated from tree ensembles. It has long been known that Mean Decrease Impurity (MDI), one of t…
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14 results for “MDI”
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.
Missing data imputation (MDI) is a fundamental problem in many scientific disciplines. Popular methods for MDI use global statistics computed from the entire data set (e.g., the feature-wise medians), or build predictive models operating independently on every instance. In this paper we propose a more general framework…
Integrating Random Forests and Generalized Linear Models for Improved Accuracy and Interpretabilitystat.ME
Combines RFs and GLMs for better accuracy and interpretable feature importance.
problem Inability to interpret random forests (RFs) due to black box nature and unstable feature importance methods.
method Reinterprets decision trees and MDI as linear regression and R² values, combining RFs and GLMs in RF+.
result MDI+ outperforms existing feature importance measures in identifying signal features and stability.
Local MDI+ improves feature importance for tree-based models, enhancing interpretability and performance.
problem Lack of instance-specific feature importance for tree-based models.
method Local MDI+ extends MDI+ framework to provide instance-specific feature importances.
result Local MDI+ outperforms existing baselines, improving predictive performance by 10%.
Second-order Approximation of Minimum Discrimination Information in Independent Component Analysisstat.ML
A new method improves ICA performance by approximating MDI.
problem Improving F astICA's performance with nonlinear functions.
method Second-order approximation of MDI for joint maximization.
result Efficiency validated through experiments compared to other ICA algorithms.
New method explains anomalies in multivariate time series data.
problem Understanding and explaining anomalies in multivariate time series data.
method Counterfactual reasoning applied to MDI-detected anomalous intervals.
result Our method accurately identifies and explains anomalies in various extreme events.
A new approach improves numerical tabular data imputation by addressing diffusion models' limitations.
problem Inaccurate and difficult training in numerical tabular data imputation.
method Kernelized Negative Entropy-regularized Wasserstein gradient flow Imputation (KnewImp) based on Wasserstein gradient flow (WGF) framework.
result KnewImp significantly outperforms existing methods in numerical tabular data imputation.
This paper analyzes Mean Decrease Impurity (MDI) variable importance in random forests.
problem Lack of interpretability in random forest variable importances.
method Analysis of Mean Decrease Impurity (MDI) in random forests.
result MDI provides a variance decomposition of the output when variables are independent and there are no interactions.
Analyzing CARTstat.ML
Decision trees with binary splits are popularly constructed using Classification and Regression Trees (CART) methodology. For binary classification and regression models, this approach recursively divides the data into two near-homogenous daughter nodes according to a split point that maximizes the reduction in sum of …
Unified view of improving tree model interpretability and debiasing feature importance.
problem Improving interpretability and debiasing feature importance in tree-based models.
method Demonstrates a common thread among bias correction methods and local explanations for trees.
result Points out a bias in explainable AI for trees algorithms due to inbag data inclusion.
ALPE improves mid-price forecasting in HFT with real-time data.
problem Real-time mid-price forecasting in high-frequency trading.
method Adaptive Learning Policy Engine (ALPE) using RL and adaptive epsilon decay.
result ALPE outperforms other models in mid-price forecasting.
Automatic detection of anomalies in space- and time-varying measurements is an important tool in several fields, e.g., fraud detection, climate analysis, or healthcare monitoring. We present an algorithm for detecting anomalous regions in multivariate spatio-temporal time-series, which allows for spotting the interesti…
Study automates feature selection and clustering for HFT stock price forecasting.
problem Manual feature selection and clustering for high-frequency trading (HFT) stock price forecasting.
method Dual competitive feature importance mechanism and clustering via shallow neural network topology.
result Enhanced forecasting ability of the RBFNN regressor through automated feature selection and clustering.