• DATA
  • MODEL
  • VALIDATION
  • HUMAN

How to generate synthetic minority samples with your AI

Connect your AI to the SMOTE Oversampling Engine to fix imbalanced datasets. It uses K-Nearest Neighbors to build new, realistic data points for your rare classes.

Ask AI about this page

Short answer

How can I create synthetic data for rare classes?

Your AI uses K-Nearest Neighbors interpolation to generate new, synthetic samples that mimic your existing minority class. This balances your dataset so your models don't ignore rare events. You'll get a new, expanded dataset ready for training.

The results of oversampling.

Where your data lands.

Once the AI finishes the interpolation, you'll have these specific outcomes.

DATA

Balanced datasets

The AI generates new rows for your minority class. You get a dataset where rare events have enough weight to be learned.

MODEL

Better training performance

By fixing the class imbalance, your AI helps prevent the model from being biased toward the majority class. This leads to better detection of rare cases.

VALIDATION

Realistic data points

The engine uses interpolation between existing points rather than just duplicating them. This ensures the new data stays within the actual feature space.

HUMAN

Manual review needed

You must inspect the synthetic samples to ensure they make sense for your specific domain. The AI creates the math, but you verify the logic.

The workflow

What your AI does when the data arrives.

The AI handles the heavy math of interpolation so you don't have to write the algorithms yourself.

  1. Analyze imbalance

    The AI looks at your provided dataset to identify which classes are underrepresented.

    generate_smote

  2. Find neighbors

    It identifies the K-Nearest Neighbors for each minority sample to understand the local data distribution.

    generate_smote

  3. Interpolate points

    The engine creates new synthetic examples by drawing lines between existing minority points.

    generate_smote

  4. Export results

    The AI provides the expanded dataset back to you for model training.

    generate_smote

Try it

Copy these to start.

Use these prompts to trigger the oversampling process.

Starting points

Replace the bracketed text with your actual file paths or data descriptions.

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

  • Generate synthetic samples for the 'fraud' class in my CSV file at /data/transactions.csv.

  • Use SMOTE to balance my dataset in this Jupyter notebook based on the 'target' column.

  • I have an imbalanced dataset in this dataframe; please oversample the minority class using K-Nearest Neighbors.

  • Look at my training data in the current directory and create new samples for the rare labels.

  • 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 AI to balance your data immediately.

Connect SMOTE Oversampling Engine to your AI

FAQ

How this task behaves.

  • 01

    Does the AI change my original data file?

    No. The AI generates a new set of synthetic samples. Your original source file remains untouched.

  • 02

    Can the AI decide which classes to oversample?

    Yes, if you ask it to find the imbalance first. You can also specify exactly which class needs more samples.

  • 03

    Will the AI clean my data before oversampling?

    No. This tool is specifically for generating synthetic samples. You need to handle cleaning and outlier removal yourself.

  • 04

    Does it just duplicate existing rows?

    No. It uses K-Nearest Neighbors interpolation to create entirely new, unique data points between existing ones.

  • 05

    Can the AI perform feature engineering?

    No. The tool focuses on generating samples based on the features you already have.

  • 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