• METRIC
  • DETECTION
  • REPORTS
  • HUMAN

How to detect prediction outliers with your AI

Connect your regression data to your AI client to spot errors. It uses error metrics to pinpoint exactly which predictions are failing.

Ask AI about this page

Short answer

How can I use RMSE and MAE to find outliers?

When RMSE is significantly higher than MAE, a few large errors are pulling your average up. Your AI compares these two values to flag the specific rows causing the skew. You get a list of problematic data points to investigate.

The results you get

Where the analysis lands.

The AI processes your regression results to isolate errors.

METRIC

Error gap analysis

The AI calculates the difference between RMSE and MAE. This tells you if your errors are consistent or driven by a few massive misses.

DETECTION

Outlier identification

The AI scans your dataset for the specific rows where the error magnitude is disproportionately high. You get the exact indices of these points.

REPORTS

Summary of skew

The AI provides a breakdown of how much the outliers are inflating your error metrics. This helps you decide if you need to prune your data.

HUMAN

Manual data cleaning

The AI flags the outliers, but you decide whether to remove them, fix them, or keep them. It leaves the final data integrity decisions to you.

The workflow

What your AI does when the data arrives.

Your AI handles the heavy math so you can focus on the data quality.

  1. Calculate errors

    The AI runs the math on your actual vs. predicted values to get the base error numbers.

    calculate_regression_metrics

  2. Compare metrics

    It looks at the relationship between the two metrics to see if the error distribution is heavy-tailed.

    calculate_regression_metrics

  3. Isolate outliers

    The AI identifies the specific data points that contribute most to the RMSE spike.

    calculate_regression_metrics

  4. Present findings

    The AI gives you a clear list of the problematic rows and explains why they are considered outliers.

    calculate_regression_metrics

Try it

Copy these to start.

Use these prompts to kick off the analysis in your chat window.

Starting points

Replace 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_7taOhFMQ4QeTOzapBajB7VxkIYnA7kpKNDVm5F0k/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": {
    "rmse-mae-calculator-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_7taOhFMQ4QeTOzapBajB7VxkIYnA7kpKNDVm5F0k/mcp"
    }
  }
}
Copy into chat04

  • Calculate the RMSE and MAE for the results in my 'predictions.csv' file.

  • Compare the RMSE and MAE in my current dataset and tell me if I have significant outliers.

  • Look at the error metrics for my 'model_output.json' and list the top 5 rows causing the highest error.

  • Based on the error gap in my spreadsheet, which specific predictions should I investigate for errors?

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Start here

Connect RMSE & MAE Calculator once, then ask.

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

Connect RMSE & MAE Calculator to your AI

FAQ

Common questions

  • 01

    Can the AI automatically delete outliers from my CSV?

    No. The AI only identifies and flags the outliers. You must manually edit or delete the data in your original file.

  • 02

    Does the AI fix my model's weights?

    No. This tool is for error analysis, not for retraining or optimizing your model parameters.

  • 03

    How does it know which points are outliers?

    It looks for a large gap between the RMSE and the MAE, which indicates that a few large errors are dominating the error profile.

  • 04

    Can I use this with any regression data?

    Yes, as long as you can provide the actual values and the predicted values to your AI client.

  • 05

    Will the AI change my original data files?

    No. The AI only reads the data to perform calculations; it does not write back to or modify your source files.

  • More questions about RMSE & MAE Calculator? The Connector page answers them. See everything the RMSE & MAE Calculator Connector can do

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