# Data Science -> Tabular Modeling

Classical and neural classifiers and regressors for tabular or structured data, including gradient boosting and tabular deep learning.

Choose a repository below only when its description, package/repository identity, task surface, and runtime intent match the request. If several candidates overlap, prefer the one whose root skill directly covers the requested workflow; then inspect its internal navigation rather than loading all candidates.

| Repo skill | Repository | Skill description |
| --- | --- | --- |
| [`autogluon`](../../../../repo-skills/autogluon/SKILL.md) | `autogluon/autogluon` | Route AutoGluon repo tasks across tabular ML, time-series forecasting, multimodal AutoML, package setup, diagnostics, and saved predictor troubleshooting. |
| [`auto-pytorch`](../../../../repo-skills/auto-pytorch/SKILL.md) | `automl/Auto-PyTorch` | Route Auto-PyTorch tabular and forecasting workflows. |
| [`numpy-ml`](../../../../repo-skills/numpy-ml/SKILL.md) | `ddbourgin/numpy-ml` | Routes numpy-ml users to the right classical ML, preprocessing, neural-component, probabilistic, and RL workflows. |
| [`limi-x`](../../../../repo-skills/limi-x/SKILL.md) | `limix-ldm-ai/LimiX` | Use LimiX for structured/tabular foundation-model inference, configuration, retrieval tuning, and benchmark-style workflows. |
| [`automl-gs`](../../../../repo-skills/automl-gs/SKILL.md) | `minimaxir/automl-gs` | Routes automl_gs tabular AutoML searches and the generated runtime artifacts that those searches produce. |
| [`mljar-supervised`](../../../../repo-skills/mljar-supervised/SKILL.md) | `mljar/mljar-supervised` | Use MLJAR AutoML for tabular classification, regression, fairness-aware training, reports, persistence, and generated Mercury apps. |
| [`igel`](../../../../repo-skills/igel/SKILL.md) | `nidhaloff/igel` | Use Igel for classic tabular ML, FastAPI serving, and AutoKeras-backed Auto-ML workflows. |
| [`cuml`](../../../../repo-skills/cuml/SKILL.md) | `NVIDIA/cuml` | cuML operating skill for GPU-accelerated classical ML, cuml.accel, Dask multi-GPU workflows, data utilities, and native source-build guidance. |
| [`serenata-de-amor`](../../../../repo-skills/serenata-de-amor/SKILL.md) | `okfn-brasil/serenata-de-amor` | Use Operação Serenata de Amor's Rosie suspicious-expense pipeline and Jarbas Django data API, setup, and data-loading workflows. |
| [`libra`](../../../../repo-skills/libra/SKILL.md) | `Palashio/libra` | Use the Libra ergonomic machine-learning client for query-driven tabular, NLP, vision, recommendation, dashboard, and analysis workflows. |
| [`plexe`](../../../../repo-skills/plexe/SKILL.md) | `plexe-ai/plexe` | Route Plexe model-building, retraining, and dashboard workflows. |
| [`tabpfn`](../../../../repo-skills/tabpfn/SKILL.md) | `PriorLabs/TabPFN` | Routes TabPFN tabular foundation-model workflows across prediction, preprocessing, batched inference, tuning, and model-management tasks. |
| [`mlxtend`](../../../../repo-skills/mlxtend/SKILL.md) | `rasbt/mlxtend` | Use mlxtend machine-learning extension utilities for estimator ensembles, evaluation, feature workflows, frequent patterns, plotting, datasets, and small helper APIs. |
| [`ml-algorithms`](../../../../repo-skills/ml-algorithms/SKILL.md) | `rushter/MLAlgorithms` | Use this skill for MLAlgorithms (`mla`) educational machine-learning implementations: classical estimators, clustering/reduction, metrics, NeuralNet building blocks, and DQN examples. |
| [`imbalanced-learn`](../../../../repo-skills/imbalanced-learn/SKILL.md) | `scikit-learn-contrib/imbalanced-learn` | Router for imbalanced-learn samplers, workflows, metrics, datasets, and balanced batch generators. |
| [`lazypredict`](../../../../repo-skills/lazypredict/SKILL.md) | `shankarpandala/lazypredict` | Use Lazy Predict for low-code model benchmarking, supervised classification and regression sweeps, time-series forecasting comparisons, CLI CSV runs, optional tuning, explainability, MLflow, Spark, and dependency troubleshooting. |
| [`data-science-python`](../../../../repo-skills/data-science-python/SKILL.md) | `ujjwalkarn/DataSciencePython` | Use the DataSciencePython tutorial/example collection through modern self-contained helpers for Python data-science resources, statsmodels logistic regression, scikit-learn Kaggle-style tabular classifiers, and Twitter JSONL extraction. |