We're training a model on tabular data but have a huge class imbalance. What's the best synthetic data generation tool for creating balanced tabular datasets?

For imbalanced tabular classification, I’d generally start with SDV (Synthetic Data Vault), particularly its CTGAN model. It’s designed specifically for synthetic tabular data and handles mixed numerical/categorical columns and complex relationships better than simply duplicating minority examples.

That said, the “best” approach depends on what you’re trying to optimize:

• SDV / CTGAN — strong general-purpose choice for generating realistic synthetic minority-class records.
• SMOTE / Borderline-SMOTE — often the best first baseline when you primarily need to rebalance a classifier rather than generate a realistic synthetic dataset. It interpolates between existing minority examples.
• TVAE (via SDV) — useful alternative when CTGAN doesn't model your data well.
• Copula-based models — worth considering when preserving statistical distributions and correlations is especially important.
• Custom generative models — potentially better for very large or unusual datasets, but considerably more engineering.

What I'd actually do

Don't blindly generate a 50/50 dataset. Create synthetic examples only for the training split, leave validation/test data untouched, and compare:

1. Original imbalanced training data
2. Class weighting
3. SMOTE
4. CTGAN-generated minority samples

Evaluate with PR-AUC, minority-class recall/precision, F1, and calibration, rather than accuracy alone.

If you tell me your number of rows, minority percentage, number of categorical/numerical columns, and whether the data is sensitive, I can recommend the specific tool/model and give you a Python implementation.

When dealing with heavy class imbalance in tabular data, turning to generative tools or oversampling frameworks is a great move. The "best" tool depends on whether you prefer a Python machine learning library (to run locally in your pipeline) or an enterprise/commercial platform.

Here is a breakdown of the top tools categorized by your workflow needs:

1. Top Open-Source Python Libraries (Best for Custom ML Pipelines)

• Synthetic Data Vault (SDV): The gold standard ecosystem for tabular data. It includes models like CTGAN, TVAE, and Copula-based models. You can filter your minority class, train a dedicated synthesizer specifically on those rare rows, and generate as many balanced synthetic minority samples as you need. Check it out at the Synthetic Data Vault Documentation.
• Gretel Synthetics: Built for developers, Gretel provides exceptional privacy-preserving models and fine-tunable architectures (like LSTM and Transformer-based models) for structured tabular data. It handles complex statistical distributions smoothly. Explore tools and APIs on Gretel.ai.
• REaLTabFormer: A newer approach that leverages Hugging Face-style GPT-2 architectures adapted for tabular data. It excels at capturing complex column interdependencies and conditional distributions, which is helpful if your minority class has subtle multi-variable patterns. Available via the REaLTabFormer PyPI page.
• Imbalanced-Learn (SMOTE / ADASYN): Don't completely overlook traditional geometric oversampling! If your dataset is mostly numeric and relationships are relatively linear, standard algorithms like SMOTE or Borderline-SMOTE from imbalanced-learn are lightning-fast alternatives to deep-learning generators.

2. Commercial / Enterprise Platforms (Best for No-Code & Data Quality Analysis)

• YData: Specifically targets data quality issues like class imbalance and distribution gaps. It provides automated data profiling and synthetic augmentation via both an SDK and a no-code interface, making it ideal if you want to inspect and heal data imbalances visually. Learn more at YData.
• MOSTLY AI: Offers highly accurate, automated synthetic data generation for structured/tabular data with strong privacy guarantees. It natively handles conditional generation, meaning you can easily instruct it to upsample a specific target label. Check out MOSTLY AI.

Recommended Strategy for Class Imbalance

Instead of generating a whole table from scratch, the most effective approach for class imbalance is conditional generation:

1. Slice your training data to isolate only the minority class rows.
2. Train a localized generative model (like CTGAN or TVAE via SDV) exclusively on that minority subset.
3. Sample from that specific generator to "top up" the minority class until your classes reach a desired ratio (e.g., 1:1 or 1:2 instead of 1:100).
4. Combine your real data with the newly generated synthetic minority rows before feeding it to your final classifier.

To help narrow down the best choice, tell me:

• What is your imbalance ratio (e.g., 99:1 vs 90:10)?
• Is your data strictly numerical/categorical , or does it include time-series/relational structures?
• Do you prefer a local Python script or a hosted platform?

When dealing with extreme class imbalance in tabular data, the "best" tool depends heavily on your dataset size, complexity, and whether traditional geometric approaches or deep-learning generators fit your pipeline.

The top-tier open-source tools and libraries explicitly well-suited for tabular data synthesis and balancing include:

1. YData-Synthetic (YData SDK)
   • Why it's great: Specifically tailored for data science workflows and data quality enhancement, YData provides built-in utilities for handling data profiling, missing values, and class imbalances. It supports advanced conditional GANs and synthesizers designed to respect tabular boundaries.
2. SDV (Synthetic Data Vault)
   • Why it's great: The industry standard for tabular synthetic data. It includes models like TVAE (Tabular Variational Autoencoder) and CTGAN (Conditional GAN). TVAE generally outperforms CTGAN in maintaining high distributional fidelity and stability on tricky or severely imbalanced real-world datasets.
3. Imbalanced-learn (SMOTE & ADASYN)
   • Why it's great: Don't completely sleep on algorithmic oversampling. For simpler tabular structures or tight compute budgets, geometric interpolation via SMOTE (or borderline-SMOTE) frequently matches or beats heavy deep-learning generators in F1-score utility without introducing hallucinated noise.
4. CTAB-GAN+
   • Why it's great: An evolution specifically built to fix standard CTGAN's shortcomings with mixed data types and class imbalances. It uses conditional sampling based on the frequency distribution of discrete columns, handling skewed distributions much more gracefully.
5. TabDDPM / GReaT (Generative Realistic Tabular data)
   • Why it's great: Newer diffusion-based approaches (TabDDPM ) and Large Language Model-based tabular approaches (GReaT , which fine-tunes transformers like GPT on tabular rows) show exceptional promise at capturing complex minority-class relationships where traditional GANs collapse or mode-drop.

To help narrow down the ideal choice, could you share:

• Roughly how many rows and columns your dataset has?
• What proportion the class imbalance is (e.g., 99:1 vs 90:10)?
• Are your features mostly numerical, categorical, or a mixed bag?

For imbalanced tabular classification, my default recommendation is SMOTE via imbalanced-learn rather than a full generative-AI synthesizer. Recent benchmarks find SMOTE particularly strong for minority-class predictive utility, while GAN-based approaches can struggle to learn sparse minority distributions.

What I'd use

• 🥇 SMOTE / Borderline-SMOTE — Best starting point when your goal is simply to balance classes and improve classifier performance. It's fast, easy to tune, and directly targets the minority class.
• 🥈 SDV + CTGAN — Best if you specifically want realistic synthetic records, including mixed categorical/numerical columns and more complex nonlinear relationships. SDV provides preprocessing, constraints, sampling, and quality evaluation around CTGAN.
• 🥉 SDV + GaussianCopula — Excellent lightweight baseline. SDV itself recommends it as the starting synthesizer for speed, quality, and customization.
• TabDDPM / other diffusion models — Worth testing if you have enough data and synthetic fidelity is important; recent benchmarks show promising predictive utility, but they're more computationally involved.

Important distinction: don't train a generative model on the entire imbalanced dataset and simply ask it for a 50/50 output. A generator can inherit the original imbalance and produce poor minority examples. Instead, condition/oversample the minority class or train a minority-focused generator, and validate the resulting samples against a held-out real test set.

If you tell me roughly (1) number of rows, (2) number of features, (3) minority/majority ratio, and (4) how many categorical vs. numerical columns, I can recommend the specific method and configuration I'd use.

For imbalanced tabular classification, my default recommendation is docs.sdv.dev, using CTGAN as the first generator to try. It has a mature Python API, supports mixed numerical/categorical columns, constraints, and synthetic-data quality evaluation.

What I'd use

• Best general-purpose tool: SDV + CTGAN

• Good for complex relationships between categorical and numerical features.

• CTGAN is specifically designed for tabular data and is available directly through SDV.

• SDV also gives you evaluation/diagnostic tooling rather than just a generator.

• If your primary goal is simply fixing class imbalance: SMOTE may actually be better

• For many classification problems, you don't need a full generative model. SMOTE directly synthesizes minority-class examples.

• A 2026 comparative study on imbalanced tabular data found SMOTE produced the best minority-class recall and F1 among the tested methods, while CTGAN/TVAE improved over the baseline but performed worse under default settings.

• If you want higher-fidelity synthetic data rather than just oversampling: SDV/CTGAN

• Particularly attractive when you need realistic combinations of many features, not merely interpolated minority examples.

One important caveat

Don't train CTGAN on the whole imbalanced dataset and then blindly ask it for a 50/50 dataset. The generator can learn the original class distribution and still underrepresent the minority class.

Instead, I'd generally:

1. Split train/test first, preserving the original test distribution.
2. Train the synthesizer using the training data.
3. Generate minority-class-focused synthetic examples.
4. Combine them with real training data.
5. Compare against a strong SMOTE/ADASYN baseline.
6. Evaluate on the untouched, naturally imbalanced test set using PR-AUC, minority recall, F1, and calibration, rather than accuracy.

If you have a very severe imbalance (e.g. 1:100+), I would start with SMOTE/ADASYN as the baseline and only move to CTGAN/SDV if you have evidence that simple interpolation isn't preserving the minority-class structure.

docs.sdv.dev ·

If you tell me your class ratio, number of rows, number of categorical/numerical features, and whether the minority class has only hundreds or thousands of examples, I can recommend the specific generator and sampling strategy.

When dealing with severe class imbalance in tabular data, the "best" tool depends heavily on whether you need statistical/distributional fidelity (modeling the entire joint distribution of the data) or purely discriminative utility (boosting minority class signals for classification).

Here are the top tools and approaches categorized by how they handle tabular class imbalance:

1. Open-Source & Python Libraries (Best for Custom Pipelines)

• SDV (Synthetic Data Vault): The gold standard open-source library for tabular synthetic data. It features models like CTGAN and TVAE (Conditional GAN and Variational Autoencoder designed for tabular data).
  • How it helps imbalance: You can condition the generation on the target label, allowing you to explicitly request/sample synthetic records for the minority class until a balanced ratio is achieved.
• YData SDK: Specifically built with data quality and data augmentation in mind. It automates data profiling and targets issues like class imbalance and missing values by generating customized synthetic records to augment minority distributions.
• TabDDPM / Diffusion Models: Emerging research shows diffusion-based models for tabular data (like TabDDPM) often outperform older GAN architectures (like CTGAN) in preserving complex minority class relationships without mode collapse.

2. Algorithmic Augmentation (Traditional vs. Generative)

• SMOTE (Synthetic Minority Over-sampling Technique) / ADASYN: While not a "GenAI deep learning tool," standard libraries like imbalanced-learn remain the fastest baseline. SMOTE interpolates between nearest neighbors in the feature space.
  • Pros: Extremely fast, lightweight, and often achieves high predictive utility F1-scores on simpler tabular datasets.
  • Cons: Can introduce noise or unrealistic feature combinations if the tabular features have tight logical constraints.
• Gretel.ai / Tonic.ai (Enterprise/API Solutions): If you need robust enterprise tooling with privacy guarantees (differential privacy) and managed infrastructure, platforms like Gretel AI offer advanced tabular models that can be prompted or tuned to upsample rare events or minority segments securely.

Which should you choose?

• If your dataset is small to medium and you want quick tabular performance, start with SMOTE or imbalanced-learn as a fast baseline.
• If your data has complex mixed data types (multimodal continuous + high-cardinality categorical) and you need deep feature interaction modeling, use SDV (specifically TVAE over CTGAN, as TVAE often yields higher distributional fidelity) , conditioned explicitly on your minority class label.

To help narrow down the ideal tool, could you share:

• The approximate size of your dataset (number of rows and columns)?
• The imbalance ratio (e.g., 99:1 vs 90:10)?
• Whether you are dealing with mostly numerical, categorical, or mixed features?

If the main goal is to fix class imbalance for a tabular classifier, I’d start with SMOTE via imbalanced-learn, rather than jumping straight to a full synthetic-data generator.

A recent 2026 comparison on imbalanced tabular data found that SMOTE produced the strongest minority-class recall and F1, outperforming CTGAN and TVAE on that benchmark.

My ranking

1. SMOTE / SMOTENC — best default for class balancing

• Very simple and fast.
• Generates minority examples from neighboring real observations.
• SMOTENC is preferable when you have categorical + numerical features.
• Particularly good when your objective is minority recall/F1, not necessarily realistic standalone synthetic data.

2. sdv.dev + CTGAN — best full synthetic-data toolkit

• Better choice if you need synthetic rows that preserve complex relationships among categorical and numerical columns.
• SDV supports CTGAN, TVAE, Gaussian Copula and other generators, plus synthetic-data quality evaluation.
• You can condition generation around particular categories, which is useful for minority-class generation.

3. Diffusion-based tabular generators — worth benchmarking for demanding problems

• Newer approaches such as TabDDPM/TabularMDLM can be strong on complex heterogeneous data, but they're substantially more involved than SMOTE/SDV.

What I'd actually do

Don't blindly generate a 50/50 synthetic dataset. A good pipeline is:

Real training data
       ↓
Split train/validation/test FIRST
       ↓
Oversample minority class in TRAIN only
       ↓
SMOTENC (baseline)
       ↓
Compare against SDV/CTGAN
       ↓
Evaluate on untouched real validation/test data

The critical point is to never synthesize/oversample before the train/test split, or you risk leakage and overly optimistic metrics.

If you tell me (1) number of rows, (2) minority percentage, (3) how many categorical vs numerical columns, and (4) Python/sklearn/PyTorch, I can recommend the specific tool/model and sampling strategy I'd use.

For handling severe class imbalance in tabular datasets, the "best" tool depends heavily on the complexity of your features (mix of continuous vs. categorical), the severity of the imbalance, and whether you prefer traditional oversampling or deep generative modeling.

The top-tier tools and methods available for tabular data balancing fall into a few clear categories:

1. The Gold Standard Open-Source Library: SDV (Synthetic Data Vault)

• Best for: Complex tabular data with mixed data types (continuous, categorical, datetime) and relational constraints.
• How it helps with imbalance: SDV includes multiple algorithms—such as TVAE (Table Variational Autoencoder) and CTGAN (Conditional GAN) . Because CTGAN and TVAE allow conditional sampling, you can explicitly prompt the generator to output rows belonging to the minority class, effectively allowing you to upsample the rare class by learning its actual underlying distribution rather than just linear interpolations.
• Get started: Check out the Synthetic Data Vault (SDV) Documentation for implementation guides.

2. The Traditional & Fast Baseline: imbalanced-learn (SMOTE & ADASYN)

• Best for: Quick baselines, smaller datasets, or primarily numerical feature spaces.
• How it helps with imbalance: While not a "deep generative AI" tool, SMOTE (Synthetic Minority Over-sampling Technique) and its variants remain the most widely used methods for class imbalance. It works in feature space by interpolating between neighboring minority class points. It’s computationally lightweight and fast, though it can struggle with heavily categorical datasets or complex multi-modal distributions.
• Get started: Explore the imbalanced-learn documentation for algorithms like SMOTE, Borderline-SMOTE, and ADASYN.

3. The Enterprise & Production Grade: Gretel.ai or Tonic.ai

• Best for: Enterprise pipelines, automated privacy/differential privacy guarantees, and production-ready APIs.
• How it helps with imbalance: Platforms like Gretel AI offer advanced tabular models (like Gretel Navigator or custom RNN/Transformer-based generators) that accurately model rare events and tail distributions in enterprise data without leaking PII. You can filter and oversample minority segments cleanly.

Which approach should you choose?

• Try TVAE or CTGAN via SDV first if your data has rich feature correlations, mixed categorical/continuous columns, and SMOTE is giving you unrealistic synthetic rows.
• Use SMOTE (from imbalanced-learn) if you need a fast, low-overhead sanity check or have a strictly numerical dataset.

To help narrow down the ideal approach, could you tell me:

• What is the ratio of your class imbalance (e.g., 99:1 vs. 90:10)?
• Is your data strictly numerical , or does it contain high-cardinality categorical variables?

When dealing with severe class imbalance in tabular data, the industry standard open-source library is SDV (Synthetic Data Vault) . It provides specialized deep learning and statistical models designed specifically to capture complex tabular structures and minority classes.

The best tools and models to consider depend on your dataset's specific size and complexity:

• CTGAN (Conditional Tabular GAN) : Available via Synthetic Data Vault (SDV) , CTGAN uses a conditional generator specifically optimized to handle discrete and continuous data imbalances. The "conditional" vector forces the model to sample from minority classes so they aren't drowned out. Best for large, complex datasets with non-linear relationships.
• TVAE (Tabular Variational Autoencoder) : Also part of the Synthetic Data Vault (SDV) ecosystem, TVAE often outperforms GANs on smaller datasets or when maximizing feature diversity in minority classes is critical. It is usually faster and more stable to train than CTGAN.
• YData SDK : An alternative data-centric AI tool specifically built to detect quality issues like class imbalance and automatically synthesize data to balance distributions via an easy-to-use SDK or no-code interface.
• Gretel Synthetics : Excellent if you need an enterprise-grade, highly scalable API approach to generating differential privacy-backed synthetic tabular data.

Tip: When using conditional models like CTGAN to fix imbalance, you can explicitly target the minority label during the sampling phase (e.g., calling sample conditioned on your rare class) to precisely dial in your desired class ratio.

To help narrow down the ideal approach, tell me:

• How large is your dataset (number of rows and columns)?
• What is the ratio of your majority to minority class?
• Are your features mostly numerical, categorical, or a mix?

If your goal is balancing a highly imbalanced classification dataset, my first choice would be sdv.dev, particularly its CTGAN synthesizer.

My recommendation

1. SDV + CTGAN — best general-purpose choice

SDV is a mature Python ecosystem specifically designed for tabular synthetic data. It supports categorical and numerical columns, constraints, evaluation, and several generation algorithms. Its CTGAN implementation is designed to model complex tabular distributions and can be used to generate additional minority-class examples.

For your use case, I'd do:

from sdv.single_table import CTGANSynthesizer

# Train only on minority-class rows
minority = train_df[train_df["target"] == 1].drop(columns=["target"])

synthesizer = CTGANSynthesizer(metadata)
synthesizer.fit(minority)

synthetic_minority = synthesizer.sample(
    num_rows=len(train_df[train_df["target"] == 0])
)

synthetic_minority["target"] = 1
balanced_train = pd.concat([train_df, synthetic_minority])

The important part is training the generator on the minority class, rather than generating an entire synthetic dataset and hoping the generator reproduces the original imbalance.

2. SDV + GaussianCopula — start here if you want something fast

SDV itself recommends GaussianCopulaSynthesizer as the starting point for speed, quality, and customization. CTGAN/TVAE are better candidates when you need higher fidelity and have enough minority examples to train a neural generator.

3. MOSTLY AI — worth considering for very large datasets

mostly.ai is particularly interesting if you're dealing with millions of rows, multiple related tables, or privacy requirements. Its SDK supports local training and generation and is explicitly optimized for efficient tabular synthesis.

One important caveat

Synthetic oversampling isn't automatically better than SMOTE/ADASYN. For a conventional ML classifier, I'd benchmark at least:

• Original data + class weights
• SMOTE
• SMOTE + Tomek/ENN
• SDV/CTGAN minority oversampling
• Possibly a simpler Gaussian-copula generator

Evaluate on a completely untouched real validation/test set, not synthetic data. The metric I'd prioritize is minority-class PR-AUC, along with recall/precision at the operating threshold—not just accuracy or ROC-AUC.

If you tell me roughly how many rows you have, the minority percentage, and whether the columns are mostly numeric/categorical, I can recommend the specific generator and oversampling strategy I'd use.