Background Childhood Anemia affects an estimated 40% of children aged 6-59 months globally and arises from heterogeneous nutritional, infectious, and socioeconomic factors that vary substantially across settings. This variability challenges the generalizability of predictive machine learning models, which often degrade under cross-population or temporal shifts. We investigated the utility a modern transformer-based tabular foundation model (TabPFN) as a complementatry framework with respect to supervised classical machine learning methods across diverse country contexts, with particular attention to data-scarce settings where surveillance capacity is most limited. Methods We conducted a multi-country prediction study using Demographic and Health Surveys (DHS) children's recode data from 16 countries spanning Africa, Asia, Latin America, the Caucasus, and the Middle East. The harmonized analytic cohort comprised of (n = 68,856)children aged 6-59 months with valid hemoglobin measurements. Anemia was defined using WHO age and altitude-adjusted thresholds and treated as a binary outcome. We trained Logistic Regression, XGBoost, and LightGBM models using standard supervised learning, and evaluated TabPFN v2.6 in an in-context learning setting. Performance was assessed using Area Under the Receiver Operating Characteristic Curve (AUC-ROC) and other standard classification metrics, with calibration evaluated via Brier score and expected calibration error (ECE). Uncertainty in performance estimates was quantified using bootstrap resampling to derive 95% confidence intervals. Robustness was assessed in a few-shot learning setting. Cross-population generalization was examined using leave-one-country-out (LOCO) validation and reverse-LOCO experiments to assess directional transferability. Subgroup analyses were conducted across five demographic strata: child age group, sex, maternal education, residence type, and household wealth quintile. Feature importance was assessed using standard linear and tree-based explainer SHAP values for the three supervised models and an adapted version of SHAP for TabPFN, aggregated across countries and examined at the country level. TabPFN also yielded the best probabilistic calibration across all 16 countries, achieving the lowest mean Brier score (0.203) and Expected Calibration Error (ECE = 0.042) of all models evaluated; LightGBM and Logistic Regression exhibited the greatest miscalibration, particularly at higher predicted probabilities. Under full-data conditions, within-country discrimination was moderate across all models (AUC-ROC 0.59-0.76) Under LOCO validation, performance declined modestly (AUC-ROC 0.58-0.69) Reverse-LOCO analyses revealed asymmetric and directional transferability, with epidemiologically diverse populations serving as more informative training sources and certain target populations remaining persistently difficult to predict regardless of model or training data.

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Given Name Surname line 2: dept. Abstract -- The lack of epidemiological data in wireless sensor networks (WSNs) is a fundamental difficulty in constructing robust models to forecast and mitigate threats like viruses and worms. Many studies have looked at different epidemic models for WSNs, focusing on the manner in which malware infections spread given the network's specific properties, including energy limits and node mobili ty. In this study, an agent - based realization of the susceptible - exposed - infected - recovered - vaccinated (SEIRV) mathematical model was employed for machine learning (ML) predictions. Using tools such as Netlogo's BehaviorSpace and Python, two epidemic synth etic datasets were generated and prepared for the application of several ML algorithms. Posed as a regression problem, the infected and recovered nodes were predicted, and the performance of these algorithms is compared using the error metrics of the train and the test sets. The predictions performed quite well, with low error metrics and high R values (0.997, 1.000, 0.999, 1.000), indicating an effective fit to the training set. The validation values were lowered (0.992, 0.998, 0.971, and 0.999), as is ty pical when evaluating model performance on unknown data. Judging from the recorded performances, support vector, linear, Lasso, Ridge, and ElasticNet regression were among the worst performing algorithms, while Random Forest, XGBoost, Decision Trees, and K nearest neighbor had the best model performances. In recent years, the globe as we know it has been changing due to bre akthroughs in numerous linked innovations including smart electrical grids [1], the IoT, long - term evolution, 5G connectivity [2] and cyber physical systems [3] such as wireless sensor networks (WSN).

We argue that progress toward general intelligence requires complementary foundation models grounded in language, the physical world, and structured data. This report presents LimiX-16M and LimiX-2M, two instantiations of our large structured-data models (LDMs). Both models treat structured data as a joint distribution over variables and missingness, thus capable of addressing a wide range of tabular tasks through query-based conditional prediction via a single model. They are pretrained using masked joint-distribution modeling with an episodic, context-conditional objective, supporting rapid, training-free adaptation at inference. We evaluate LimiX models across 11 large structured-data benchmarks with broad regimes of sample size, feature dimensionality, class number, categorical-to-numerical feature ratio, missingness, and sample-to-feature ratios. LimiX-16M consistently surpasses strong baselines, as shown in Figure 1 and Figure 2. The superiority holds across a wide range of tasks, such as classification, regression, missing value imputation, and data generation, often by substantial margins, while avoiding task-specific architectures or bespoke training per task. Notably, LimiX-2M delivers strong results under tight compute and memory budgets. We also present the first scaling law study for LDMs, revealing how data and model scaling jointly influence downstream performance and offering quantitative guidance for tabular foundation modeling. All LimiX models are publicly accessible under Apache 2.0.

With the growing popularity of deep learning and foundation models for tabular data, the need for standardized and reliable benchmarks is higher than ever. However, current benchmarks are static. Their design is not updated even if flaws are discovered, model versions are updated, or new models are released. To address this, we introduce TabArena, the first continuously maintained living tabular benchmarking system. To launch TabArena, we manually curate a representative collection of datasets and well-implemented models, conduct a large-scale benchmarking study to initialize a public leaderboard, and assemble a team of experienced maintainers. Our results highlight the influence of validation method and ensembling of hyperparameter configurations to benchmark models at their full potential. While gradient-boosted trees are still strong contenders on practical tabular datasets, we observe that deep learning methods have caught up under larger time budgets with ensembling. At the same time, foundation models excel on smaller datasets. Finally, we show that ensembles across models advance the state-of-the-art in tabular machine learning. We observe that some deep learning models are overrepresented in cross-model ensembles due to validation set overfitting, and we encourage model developers to address this issue. We launch TabArena with a public leaderboard, reproducible code, and maintenance protocols to create a living benchmark available at https://tabarena.ai.

Deep Neural Networks (DNNs) have revolutionized artificial intelligence, achieving impressive results on diverse data types, including images, videos, and texts.

This study explores the explainability capabilities of large language models (LLMs), when employed to autonomously generate machine learning (ML) solutions. We examine two classification tasks: (i) a binary classification problem focused on predicting driver alertness states, and (ii) a multilabel classification problem based on the yeast dataset. Three state-of-the-art LLMs (i.e. OpenAI GPT, Anthropic Claude, and DeepSeek) are prompted to design training pipelines for four common classifiers: Random Forest, XGBoost, Multilayer Perceptron, and Long Short-Term Memory networks. The generated models are evaluated in terms of predictive performance (recall, precision, and F1-score) and explainability using SHAP (SHapley Additive exPlanations). Specifically, we measure Average SHAP Fidelity (Mean Squared Error between SHAP approximations and model outputs) and Average SHAP Sparsity (number of features deemed influential). The results reveal that LLMs are capable of producing effective and interpretable models, achieving high fidelity and consistent sparsity, highlighting their potential as automated tools for interpretable ML pipeline generation. The results show that LLMs can produce effective, interpretable pipelines with high fidelity and consistent sparsity, closely matching manually engineered baselines.

Understanding the real-world effects of recreational drug use remains a critical challenge in public health and biomedical research, especially as traditional surveillance systems often underrepresent user experiences. In this study, we leverage social media (specifically Twitter) as a rich and unfiltered source of user-reported effects associated with three emerging psychoactive substances: ecstasy, GHB, and 2C-B. By combining a curated list of slang terms with biomedical concept extraction via MetaMap, we identified and weakly annotated over 92,000 tweets mentioning these substances. Each tweet was labeled with a polarity reflecting whether it reported a positive or negative effect, following an expert-guided heuristic process. We then performed descriptive and comparative analyses of the reported phenotypic outcomes across substances and trained multiple machine learning classifiers to predict polarity from tweet content, accounting for strong class imbalance using techniques such as cost-sensitive learning and synthetic oversampling. The top performance on the test set was obtained from eXtreme Gradient Boosting with cost-sensitive learning (F1 = 0.885, AUPRC = 0.934). Our findings reveal that Twitter enables the detection of substance-specific phenotypic effects, and that polarity classification models can support real-time pharmacovigilance and drug effect characterization with high accuracy.

This paper introduces a new Python package specifically designed to address calibration of probabilistic classifiers under dataset shift. The method is demonstrated in binary and multi-class settings and its effectiveness is measured against a number of existing post-hoc calibration methods. The empirical results are promising and suggest that our technique can be helpful in a variety of settings for batch and online learning classification problems where the underlying data distribution changes between the training and test sets.