Transformer learns shipping costs more accurately than traditional methods.
problem Inaccurate shipping cost estimates lead to poor financial decisions.
method Proposes Rate Card Transformer (RCT) using self-attention to encode shipping information.
result Cost predictions made by RCT have 28.82% less error compared to GBDT models.
This work analyzes tree-based methods from a ranking perspective, providing insights and new statistics.
problem Understanding the effectiveness of tree-based methods in finite-sample settings, especially symbolic feature selection.
method Local ranking perspective, finite-sample analysis, oracle bounds, posterior contraction results, concordant divergence statistics.
result New insights and statistics for evaluating symbolic feature mappings.
Generative model for tabular data density regression.
problem Estimating conditional distribution of outcomes given covariates.
method Tree-based flow model for efficient sampling and likelihood evaluation.
result Our method achieves comparable or superior performance with reduced training and sampling costs.
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%.
Study improves probabilistic circuits using transformations for better predictions.
problem Predictive limitations of probabilistic circuits in robotic scenarios.
method Integrates transformations into joint probability trees, extending their capabilities.
result Achieves higher likelihoods with fewer parameters on various data sets.
New trees-based models handle correlated data better.
problem Standard trees-based models ignore correlation structure.
method Explicitly accounts for correlation structure in splitting criterion, stopping rules, and fitted values.
result New approach superior to standard models in simulations and real data.
Study reviews tree-based methods and introduces new ensemble strategies.
problem Improving the efficiency and performance of tree-based machine learning models.
method Review of tree-based methods, introduction of ISLE framework, ARM model combination strategy, and modified ISLEs.
result Performance evaluation of modified ISLEs on real data sets.
The main goal of this paper is to study the geometric structures associated with the representation of tensors in subspace based formats. To do this we use a property of the so-called minimal subspaces which allows us to describe the tensor representation by means of a rooted tree. By using the tree structure and the d…
New method improves feature selection in tree-based models.
problem Previous feature selection methods in tree-based models lack sufficient regularization and sub-optimal performance.
method Developed a new gain penalization approach for tree-based models that allows for flexible feature-specific importance weights.
result The new method improves out-of-sample performance, especially with correlated features.
Enhanced tree-based classifiers use derivatives and geometry for better function classification.
problem Improving classification of high-dimensional time series data.
method Integrates Functional Data Analysis with tree-based ensemble techniques, leveraging derivative and geometric features.
result Significant improvements over traditional approaches in function classification.
Simulation study evaluates tree-based imputation methods for multi-level data.
problem Ignoring dependencies in hierarchical data can compromise imputation accuracy.
method Chained Random Forests and Extreme Gradient Boosting (mixgb) adapted for multi-level data.
result Adapted boosting methods outperform traditional MICE for Level-1 variables at higher missingness rates.
Extends geometrical description of tensor manifolds in tree-based formats.
problem Geometrical description of tensor manifolds in tree-based formats.
method Provided a new geometrical description of manifolds of tensors in tree-based format.
result Geometrical description compatible with Tucker format.
Study forecasts vegetable prices in Nepal using a novel index and ensemble model.
problem High volatility and cultural influences on agricultural commodity prices.
method Developed KVPI, created features, evaluated multiple models, introduced Momentum-Corrected Online Stacking Ensemble.
result Achieved RMSE of 1.771, MAPE of 0.68%, and R-squared of 0.845 at 90-day horizon.
EmDT generates synthetic fraud data to improve detection accuracy.
problem Imbalanced datasets in fraud detection lead to poor performance on rare fraudulent transactions.
method EmDT uses UMAP clustering to identify fraudulent patterns and a Transformer denoising network to generate synthetic data.
result EmDT significantly improves classification performance compared to existing methods.
The paper introduces a method to control false splits in tree-based data aggregation.
problem Identifying the correct subgroups to treat as a single entity in tree-based data.
method Introduces the 'false split rate' and proposes a multiple hypothesis testing algorithm for tree-based aggregation.
result The proposed algorithm controls the false split rate, demonstrating its effectiveness on stock volatility and taxi fare data.
Tree-based models outperform deep learning on tabular data, especially for medium-sized datasets.
problem Understanding why tree-based models outperform deep learning on tabular data.
method Extensive benchmarks of tree-based and deep learning models on 45 datasets, accounting for hyperparameters.
result Tree-based models remain state-of-the-art on medium-sized tabular data, even without hyperparameter optimization.
Develops fair feature importance scores for tree-based models to interpret fairness.
problem Ensuring fairness in machine learning models, especially tree-based ones.
method Inspired by decision trees, proposes a novel fair feature importance score based on mean decrease in group bias.
result Valid interpretations of fairness for tree-based ensembles and surrogates of other ML systems.
We present the first tree-based regressor whose convergence rate depends only on the intrinsic dimension of the data, namely its Assouad dimension. The regressor uses the RPtree partitioning procedure, a simple randomized variant of k-d trees.
FREEtree improves tree-based methods for correlated longitudinal data.
problem Poor performance of Random Forests in high dimensional longitudinal data with correlated features.
method FREEtree uses a piecewise random effects model and clustering with WGCNA to select features and maintain interpretability.
result FREEtree outperforms other tree-based methods in prediction and feature selection accuracy.
Tree-based model averaging improves CATE estimation from diverse sites.
problem Limited sample size and privacy concerns prevent accurate personalized treatment effect estimation.
method Tree-based model averaging approach to estimate CATEs from multiple heterogeneous sites.
result Improved accuracy in estimating conditional average treatment effects (CATEs) across sites.
Improved Shapley Values for tree-based models, more accurate than existing methods.
problem Inaccurate Shapley Values in tree-based models leading to poor explanations.
method Introduced two new estimators exploiting tree structure, derived correct approach for categorical variables.
result More accurate Shapley Values for tree-based models, demonstrated through simulations.
One advantage of decision tree based methods like random forests is their ability to natively handle categorical predictors without having to first transform them (e.g., by using feature engineering techniques). However, in this paper, we show how this capability can lead to an inherent "absent levels" problem for deci…
A new tree-based model for varying coefficients using CGBM.
problem Modeling varying coefficients with high dimensionality and complex interactions.
method Tree-based varying coefficient model with CGBM for varying coefficients, dimension-wise early stopping, and feature importance scores.
result The model produces comparable out-of-sample loss to neural networks, demonstrating effectiveness.
A post-hoc framework improves model performance by calibrating different feature spaces.
problem Improving AUC performance on binary classification tasks for overconfident models.
method Identifies heterogeneous partitions of the feature space and applies post-hoc calibration techniques to each partition.
result Theoretical optimality of the framework for any model, demonstrated on deep neural networks.
TreeDOX predicts chaotic systems without hyperparameter tuning.
problem Forecasting chaotic systems requires hyperparameter tuning, limiting adoption.
method TreeDOX uses time delay overembedding and Extra-Trees Regressors.
result TreeDOX achieves state-of-the-art performance on chaotic systems.
Machine-learned models are often described as "black boxes". In many real-world applications however, models may have to sacrifice predictive power in favour of human-interpretability. When this is the case, feature engineering becomes a crucial task, which requires significant and time-consuming human effort. Whilst s…
A new approach uses partial likelihood to improve tree-based density estimation and inference.
problem Inference on tree-based models suffers from overfitting and reduced efficiency due to data-independent partitioning.
method Proposes a partial likelihood approach to data-dependent partitioning of tree-based models.
result Significant gains in estimation accuracy and computational efficiency from adopting partial likelihood.
Study compares tree-based imputation methods to MICE PMM for missing data.
problem Missing data in empirical studies.
method Various imputation methods including MICE PMM, RF, missRanger, and MIXGBoost.
result Tree-based imputations, especially RF and missRanger with PMM, perform better.
In short, our experiments suggest that yes, on average, rotation forest is better than the most common alternatives when all the attributes are real-valued. Rotation forest is a tree based ensemble that performs transforms on subsets of attributes prior to constructing each tree. We present an empirical comparison of c…
To identify and classify toxic online commentary, the modern tools of data science transform raw text into key features from which either thresholding or learning algorithms can make predictions for monitoring offensive conversations. We systematically evaluate 62 classifiers representing 19 major algorithmic families …
SCORE improves tree-based predictions with boosted residual extraTrees.
problem Improving tree-based prediction models with reduced errors.
method Inspired by representation learning, SCORE uses boosting, regularized regression, and variable selection.
result SCORE provides comparable or superior performance compared to other models.
A new tree-based model improves uncertainty estimation in sequential optimization.
problem Improving uncertainty estimation in sequential model-based optimization.
method Proposed a new ensemble of randomized trees (BwO forest) with bagging and oversampling.
result BwO forest outperforms existing tree-based models in various optimization scenarios.
nTreeClus clusters categorical sequences using tree-based learners and k-mers.
problem Challenges in clustering categorical and sequential data.
method nTreeClus uses Tree-based Learners, k-mers, and autoregressive models for categorical time series.
result nTreeClus outperformed baseline methods in various validation metrics.
We propose a modification that corrects for split-improvement variable importance measures in Random Forests and other tree-based methods. These methods have been shown to be biased towards increasing the importance of features with more potential splits. We show that by appropriately incorporating split-improvement as…
Although adversarial examples and model robustness have been extensively studied in the context of linear models and neural networks, research on this issue in tree-based models and how to make tree-based models robust against adversarial examples is still limited. In this paper, we show that tree based models are also…
Smooth fractal trees via analytic generators, preserving combinatorial and geometric properties.
problem Constructing smooth fractal trees from discrete models.
method Using analytic generator fields to integrate smooth vector fields in an internal state space, generating geometric curves as projections of generator trajectories.
result Analytic generators can represent any discrete tree specification and preserve the asymptotic limit geometry.
The paper predicts travel times using tree-based ensembles.
problem Predicting travel times between urban points over short and long horizons.
method Tree-based ensemble methods trained on taxi trip records with additional features from weather and routing data.
result Adding routing data improves model performance and short-term predictions require less data.
Machine learning models classify celestial objects like pulsars and black holes.
problem Classifying high-energy celestial objects using photometric data.
method Applied tree-based models and RNN to classify pulsars and black holes.
result RNN showed potential for real-time object discrimination and classification.
Flexible copula model using implicit generative neural networks.
problem Limited flexibility of parametric copulas and curse of dimensionality in non-parametric methods.
method Implicit generative neural networks to model high-dimensional copula distributions with unspecified marginals.
result Demonstrated flexibility and performance on various datasets.
Tree-based regularization improves latent variable inference from related datasets.
problem Inferring latent variables from multiple related datasets in causal systems.
method Tree-Based Regularization (TBR) for sparse changes across environments.
result TBR identifies true latent variables up to simple transformations under sparse changes.
New research finds tree-based GAMs are most trustworthy and fair.
problem Variability in GAM algorithms leads to inconsistent models.
method Quantitative and qualitative analysis of various GAM algorithms.
result Tree-based GAMs are the most trustworthy and fair.
Tree-based models biased when trained on imbalanced data, requiring new calibration methods.
problem Bias in tree-based models trained on imbalanced datasets.
method Analytical calibration of random forest models, demonstrating bias in decision trees.
result Calibrating tree-based models on imbalanced data negatively impacts predictions, especially for the minority class.
This paper is concerned with the approximation of high-dimensional functions in a statistical learning setting, by empirical risk minimization over model classes of functions in tree-based tensor format. These are particular classes of rank-structured functions that can be seen as deep neural networks with a sparse arc…
Tree-based synthesis improves forecast accuracy in GDP and inflation.
problem Improving forecast accuracy in GDP and inflation.
method Developed a nonparametric synthesis function using regression trees.
result Tree-based synthesis leads to improved forecast accuracy.
Decision trees improve performance in various fields.
problem Improving performance in machine learning and data mining.
method Introduce tree-based models and training techniques.
result Highlight probabilistic characteristics and practical benefits.
Enhances functional classifier performance with new tree-based methods and unbiased feature importance assessment.
problem Challenges of high-dimensional functional data and biased feature importance assessment.
method Augmented functional classification trees and random forests with ad-hoc conditional permutations for unbiased feature importance.
result Significant enhancement in predictive power of functional classifiers through new feature importance assessment.
We consider the problem of the estimation of a high-dimensional probability distribution from i.i.d. samples of the distribution using model classes of functions in tree-based tensor formats, a particular case of tensor networks associated with a dimension partition tree. The distribution is assumed to admit a density …
Study optimizes tree-based models for better alignment of predicted scores and actual probabilities.
problem Traditional calibration metrics fail to align predicted scores with actual probabilities when score distributions deviate from the underlying data.
method Optimizes tree-based models (Random Forest, XGBoost) using Kullback-Leibler (KL) divergence to minimize the difference between predicted and true probability distributions.
result Optimized tree-based models yield superior alignment between predicted scores and actual probabilities without significant performance loss.