• FP COUNTS
  • FN COUNTS
  • CLASS BIAS
  • HUMAN REVIEW

How to analyze classification error rates with your AI

Connect your prediction data once. Your AI then handles the math to find exactly where your model is failing.

Ask AI about this page

Short answer

How can I find my model's specific error types?

Your AI uses the prediction data to compute a confusion matrix, which breaks down every False Positive and False Negative. You get a clear view of which classes your model confuses most often.

Error breakdown.

Where your error data lands.

The AI processes your raw predictions into actionable error categories.

FP COUNTS

False Positive totals

The AI identifies every time your model predicted a class that wasn't there. You'll see exactly which labels are triggering false alarms.

FN COUNTS

False Negative totals

The AI flags every instance where the model missed a target. This shows you which specific categories are being overlooked.

CLASS BIAS

Identify class imbalance

By comparing error counts across labels, the AI highlights if your model is biased toward specific outcomes. You'll see which classes are over or under-represented in errors.

HUMAN REVIEW

Manual error audit

The AI isolates the specific rows of data that caused the errors. You take these specific examples to manually inspect why the model failed.

The workflow

What your AI does when the data arrives.

The AI moves from raw prediction lists to a structured error breakdown.

  1. Ingest predictions

    Your AI reads the prediction logs or spreadsheets you provide to align actual values with predicted values.

    calculate_confusion_matrix

  2. Compute error counts

    The AI runs the math to separate true hits from false alarms and misses.

    calculate_confusion_matrix

  3. Categorize failures

    The AI groups the errors by class so you can see if one specific label is causing most of the noise.

    calculate_confusion_matrix

  4. Isolate problematic rows

    The AI identifies the specific data points that resulted in a mismatch for your review.

    calculate_confusion_matrix

Try it

Copy these to start.

Use these prompts to trigger the analysis immediately.

Starting points

Swap out the bracketed text with your actual 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_lcKPfQMdEs9vOL0QvMD7tp5xcmCCFVUKYwiOuwlI/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": {
    "confusion-matrix-engine-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_lcKPfQMdEs9vOL0QvMD7tp5xcmCCFVUKYwiOuwlI/mcp"
    }
  }
}
Copy into chat04

  • Calculate the confusion matrix for the prediction_results.csv file.

  • Show me the False Positive counts for the 'spam' class in my model output sheet.

  • Analyze the error rates in the recent test_logs.json and tell me which class has the most False Negatives.

  • Find the specific rows in my dataset where the model predicted 'cat' but the actual label was 'dog'.

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

Start here

Connect Confusion Matrix Engine once, then ask.

Just link the MCP to your client. Your credentials stay encrypted, and you can start asking questions about your data right away.

Connect Confusion Matrix Engine to your AI

FAQ

How this task behaves.

  • 01

    Can the AI fix the model errors for me?

    No. The AI only identifies and counts the errors. You have to retrain or adjust your model yourself.

  • 02

    Does the AI change my original prediction data?

    No. The AI only reads your data to perform calculations. It never modifies your source files or databases.

  • 03

    Can I use this with any prediction format?

    The AI works with any data format your client can read and pass to the tool, such as CSV or JSON.

  • 04

    Will the AI delete my error logs?

    No. This MCP has no capability to delete or overwrite your data.

  • 05

    Does it calculate precision and recall too?

    Yes, because those metrics are derived from the same confusion matrix counts the AI computes.

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

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