• DATA
  • STATS
  • READY
  • HUMAN

How to balance imbalanced datasets with your AI

Connect this MCP to your AI client to fix skewed class distributions. Your agent handles the math to create synthetic data that mirrors your real samples.

Ask AI about this page

Short answer

How do I fix imbalanced data for my models?

Your AI uses the SMOTE Oversampling Engine to generate new, synthetic examples for your minority class. This creates a balanced distribution so your model doesn't ignore rare events. You'll get a new, augmented dataset ready for training.

The results of balancing.

What you get from the AI.

Once the process finishes, your agent delivers these specific outcomes.

DATA

Synthetic samples created

The engine generates new rows for your minority class. These samples follow the statistical patterns of your existing data.

STATS

Balanced class distribution

The AI adjusts the ratio between your majority and minority classes. This prevents your model from being biased toward the common cases.

READY

Augmented dataset output

You receive a complete, expanded dataset. It's ready to be fed directly into your training pipeline.

HUMAN

Validation required

You must still inspect the synthetic samples. The AI handles the math, but you decide if the new data makes sense for your specific domain.

The workflow

What your AI does when the data arrives.

The AI takes your skewed dataset and applies statistical interpolation to fill the gaps.

  1. Analyze distribution

    Your agent scans your dataset to identify which classes are underrepresented.

    generate_smote

  2. Calculate neighbors

    The engine finds the nearest neighbors for each minority sample to understand the local data structure.

    generate_smote

  3. Synthesize samples

    The AI creates new data points by interpolating between existing minority samples.

    generate_smote

  4. Finalize dataset

    The process wraps up by merging the new synthetic rows with your original data.

    generate_smote

Try it

Copy these to start.

Use these prompts to trigger the balancing process.

Starting points

Replace the bracketed text with your specific file names or data sources.

Native V8 Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_Xu8tNfSu2oJQMkax65i8ODXUxkKCb3ZJKsseLvia/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — Native V8 capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "smote-oversampling-engine-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_Xu8tNfSu2oJQMkax65i8ODXUxkKCb3ZJKsseLvia/mcp"
    }
  }
}
Copy into chat04

  • Use the SMOTE Oversampling Engine to balance the 'customer_churn.csv' file.

  • My 'fraud_detection' dataset is heavily skewed. Can you generate synthetic samples for the minority class?

  • Look at the 'sensor_data' dataframe and apply SMOTE to fix the class imbalance.

  • I have a dataset in 'training_set.parquet' where the target class is rare. Balance it for me.

  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Start here

Connect SMOTE Oversampling Engine once, then ask.

Just link the MCP to your client. Your credentials stay encrypted, and you can start asking your agent to process data immediately.

Connect SMOTE Oversampling Engine to your AI

FAQ

How the balancing works

  • 01

    Does the AI change my original data?

    No. The AI generates a new, augmented version of your dataset. Your original source file remains untouched.

  • 02

    Can the AI decide which sampling method is best?

    The tool specifically performs SMOTE. It does not automatically switch to other methods like ADASYN or undersampling unless you ask it to use a different tool.

  • 03

    Will this fix a model that is already poorly trained?

    No. This tool only fixes the data distribution. You still need to retrain your model using the new, balanced dataset.

  • 04

    Does the AI check if the synthetic data is realistic?

    The AI uses statistical interpolation to ensure the data is mathematically valid. However, it cannot know if the data is physically or logically realistic in the real world; you have to verify that.

  • 05

    Can I use this on text data?

    No. This tool is designed for numerical feature sets. It cannot generate synthetic text or natural language samples.

  • More questions about SMOTE Oversampling Engine? The Connector page answers them. See everything the SMOTE Oversampling Engine Connector can do

Connect Native V8 to Claude, Cursor, ChatGPT & more