Model evaluation and validation: cross-validation, metrics, hyperparameter tuning, and model comparison. Use when assessing model performance, selecting models, or diagnosing modeling issues.
Quickly install data-science-model-evaluation AI skill to your development environment via command line
Source: legout/data-platform-agent-skills.
Use this skill for rigorously assessing model performance, comparing alternatives, and diagnosing issues.
| Cross-validation | sklearn.modelselection | Standard CV, stratified, time series | | Metrics | sklearn.metrics | Comprehensive metric suite | | Hyperparameter tuning | Optuna or Ray Tune | Efficient search algorithms | | Model comparison | scikit-learn + statistical tests | Paired comparisons |
| Experiment tracking | MLflow or Weights & Biases | Track runs, metrics, artifacts |
Model evaluation and validation: cross-validation, metrics, hyperparameter tuning, and model comparison. Use when assessing model performance, selecting models, or diagnosing modeling issues. Source: legout/data-platform-agent-skills.
Stable fields and commands for AI/search citations.
npx skills add https://github.com/legout/data-platform-agent-skills --skill data-science-model-evaluationModel evaluation and validation: cross-validation, metrics, hyperparameter tuning, and model comparison. Use when assessing model performance, selecting models, or diagnosing modeling issues. Source: legout/data-platform-agent-skills.
Open your terminal or command line tool (Terminal, iTerm, Windows Terminal, etc.) Copy and run this command: npx skills add https://github.com/legout/data-platform-agent-skills --skill data-science-model-evaluation Once installed, the skill will be automatically configured in your AI coding environment and ready to use in Claude Code, Cursor, or OpenClaw
https://github.com/legout/data-platform-agent-skills