• RANKING
  • METRICS
  • ANALYSIS
  • HUMAN

How to compare binary classifier performance with your AI

Connect this MCP to your AI client to run mathematical comparisons across different model outputs. It handles the heavy lifting of metric calculation so you can focus on the results.

Ask AI about this page

Short answer

How do I compare binary classifier performance with my AI?

Give your AI the ground truth labels and the probability arrays from your models. The agent calculates the exact AUC for each array to show you which model has the best predictive power. You'll get a clear ranking of model performance.

The outcomes.

What you get from the comparison.

The AI processes your data to produce these specific results.

RANKING

Model performance order

The AI compares the calculated AUC values to tell you which model is statistically superior for your specific dataset.

METRICS

Exact AUC scores

You get precise numerical values for every probability array provided, not just a general sense of which is better.

ANALYSIS

Data-driven insights

The AI interprets the scores to explain how much better one model's ability to distinguish classes is compared to another.

HUMAN

Manual threshold tuning

The AI provides the scores, but you decide which specific probability threshold is best for your actual business use case.

The workflow

What your AI does when the data arrives.

The AI acts as a mathematical validator for your model outputs.

  1. Data ingestion

    Your AI reads the ground truth labels and the various probability arrays you've uploaded or pasted into the chat.

    calculate_roc_auc

  2. Metric calculation

    The agent runs the math to find the Area Under the Receiver Operating Characteristic curve for every input.

    calculate_roc_auc

  3. Comparative ranking

    The AI organizes the resulting scores to identify which model provides the highest discriminative power.

    calculate_roc_auc

  4. Result reporting

    The agent presents the final AUC values and the winner in a clean format for your review.

    calculate_roc_auc

Try it

Copy these to start.

Use these prompts to kick off your evaluation.

Starting points

Swap out the bracketed text with your actual data arrays or file paths.

Native V8 Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_VDeZcIBi8DFwURcnalpzs8zfvNkZF1ZSn2YpeI8w/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": {
    "roc-auc-evaluator-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_VDeZcIBi8DFwURcnalpzs8zfvNkZF1ZSn2YpeI8w/mcp"
    }
  }
}
Copy into chat04

  • Compare these three probability arrays against this ground truth: [array1], [array2], [array3] with labels [labels].

  • Calculate the ROC AUC for the model outputs in my 'model_results.csv' file against the ground truth in 'labels.csv'.

  • Which of these two models is performing better based on their AUC scores? [array_a], [array_b] vs [labels].

  • Run an AUC evaluation on the probability scores found in my current workspace directory.

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

Start here

Connect ROC AUC Evaluator once, then ask.

Just click the link to connect the MCP to your client. Your credentials stay encrypted, and you can start running math immediately.

Connect ROC AUC Evaluator to your AI

FAQ

How the evaluator behaves

  • 01

    Can the AI change my original data files?

    No. The AI only reads your data to perform calculations; it cannot modify or overwrite your source files.

  • 02

    Does it work with any model output?

    Yes, as long as the output is a probability array or a score that can be compared against ground truth labels.

  • 03

    Can the AI plot the ROC curves for me?

    No. This MCP is strictly for calculating the AUC metric. It does not generate visual plots or graphs.

  • 04

    Can I compare more than two models at once?

    Yes. You can provide multiple probability arrays in a single request to compare them all against the same labels.

  • 05

    Will the AI decide which model to deploy?

    No. The AI provides the mathematical evidence, but the final decision to deploy a model remains entirely with you.

  • More questions about ROC AUC Evaluator? The Connector page answers them. See everything the ROC AUC Evaluator Connector can do

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