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supervised-learning

Builds, tunes and evaluates classification and regression models on tabular data with cross-validated selection.

1|1|Updated Nov 18, 2025
Search Tags:#regression#classification#cross-validation#production-readiness#model-evaluation#hyperparameter-tuning#supervised-learning
One-click install
npx skills add https://github.com/pluginagentmarketplace/custom-plugin-machine-learning --skill supervised-learning
Or copy as Structured Prompt for Agent▌
Please help me install this Agent Skill.
Skill: supervised-learning
Source: https://github.com/pluginagentmarketplace/custom-plugin-machine-learning/tree/main/skills/supervised-learning
Command: npx skills add https://github.com/pluginagentmarketplace/custom-plugin-machine-learning --skill supervised-learning

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires yaml, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Production-grade supervised learning workflows often require end-to-end processes from data prep to model evaluation, hyperparameter tuning, and deployment readiness, which are error-prone when implemented ad-hoc.

Core Features & Use Cases

  • End-to-end supervised learning pipelines: data preprocessing, model training, evaluation
  • Hyperparameter tuning and model selection for classification and regression tasks
  • Production-ready deployment and monitoring readiness with observable metrics

Quick Start

Provide a labeled dataset and request a baseline classifier or regressor to begin evaluation.

Frequently Asked Questions about supervised-learning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a supervised learning pipeline for production deployment?▌

A production-ready supervised learning pipeline handles data preprocessing, model training, cross-validated evaluation, and hyperparameter tuning for classification and regression tasks. It captures observable metrics like training time, cv_score, and model size to ensure deployment readiness.

What is the best way to perform hyperparameter tuning and model selection on tabular data?▌

Hyperparameter tuning and model selection on tabular data are best handled through cross-validated model evaluation. This approach systematically tests parameter configurations across classification and regression models to identify the optimal performer before deployment.

How does cross-validation work for evaluating classification and regression models?▌

Cross-validation evaluates classification and regression models by partitioning tabular data into multiple subsets, training on some while testing on others. This deterministic validation strategy generates a reliable cv_score to prevent overfitting during model selection.

Do I need a deterministic training pipeline to ensure production-readiness for ML models?▌

Yes, a deterministic training pipeline is required to ensure production-readiness for ML models. Deterministic pipelines provide reproducible results for classification and regression tasks, while observability hooks capture metrics like training time and model size for monitoring.

Can I use this workflow for both classification and regression tasks on enterprise tabular data?▌

Yes, the workflow supports both classification and regression tasks on enterprise tabular data. It provides end-to-end processing from data preprocessing to cross-validated model selection, specifically designed for practical deployment-ready workflows in enterprise environments.

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