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Evaluate an ML model without leakage or bias v1.0.0

Use this resource to evaluate, compare or debug the performance of a machine learning model

New

  • 351 downloads
  • 230 likes
  • 230 saves

Free

Install with the CLI

In a terminal, from the folder you want:

npx --yes palatekit@latest install ml-model-evaluation-methods

With no option, the skill goes to ~/.agents/skills/; add --agent claude-code (or codex, cursor…) to target an agent.

First time: connect the CLI with

npx --yes palatekit@latest login

The agent sets the evaluation checklist, picks suitable splits and metrics, then checks differences with statistical tests. You get a conclusion based on reliable measurements.

Mostly picked by

Updated November 2, 2026

Card for the “Evaluate an ML model without leakage or bias” skill

Details

Works in
Install guides
Generates with
Your agent's model
Licence
MIT

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What you get

  • SKILL.md: design checklist, splits and leakage detection
  • SKILL.md: metric choice, statistical comparisons, calibration and error analysis

Good fit for

  • You compare two models with a confidence interval
  • You want to catch data leakage before deployment

Not for

    Tags

    • evaluation
    • metrics
    • validation
    • bias
    • statistics

    About

    You say

    “Check whether my classifier's improvement is real or due to chance.”

    Card for the “Evaluate an ML model without leakage or bias” skill

    Skill published by josiahsiegel under the MIT license, delivered in its original version.

    Show more

    How to use it

    1. Install the skill in your agent's skills folder
    2. Ask your agent to evaluate, compare or debug the performance of a machine learning model
    3. Review and adjust the result

    Content preview

    Files

    • LICENSE1.1 KB
    • NOTICE-PALATEKIT.md0.5 KB
    • SKILL.md10.2 KB

    Origin and licence

    Listed unmodified

    Author
    josiahsiegel
    Source
    github.com/josiahsiegel/claude-plugin-marketplace/tree/5a1b1123b9e50aa9a66a61005ca6fe012cc7442d/plugins/ml-master/skills/ml-evaluation
    Licence
    MIT

    Notice

    « ml-evaluation » (josiahsiegel, MIT) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.

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