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Sir-chawakorn
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train-evaluate-ml-model

Train and evaluate machine learning models on tabular data with leakage prevention and cross-validation.

9|3|Updated Jun 13, 2026
Search Tags:#data science#machine learning#model evaluation#predictive modeling#data leakage#scikit-learn
One-click install
npx skills add https://github.com/Sir-chawakorn/sanook-cli --skill train-evaluate-ml-model
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: train-evaluate-ml-model
Source: https://github.com/Sir-chawakorn/sanook-cli/tree/main/skills/train-evaluate-ml-model
Command: npx skills add https://github.com/Sir-chawakorn/sanook-cli --skill train-evaluate-ml-model

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill addresses the critical challenge of building machine learning models that are statistically sound and business-aligned, specifically preventing common pitfalls like data leakage, overfitting, and improper metric selection.

Core Features & Use Cases

  • Leakage-Safe Modeling: Implements rigorous Pipeline-based workflows to ensure feature engineering and scaling are fit only on training data.
  • Business-Aligned Evaluation: Provides a framework for selecting metrics based on real-world costs (e.g., PR-AUC for imbalanced fraud detection) rather than misleading accuracy.
  • Use Case: Use this to train a churn prediction model where you need to balance precision and recall, ensuring your validation strategy correctly handles temporal data or grouped entities to avoid future-leaking.

Quick Start

Use the train-evaluate-ml-model skill to fit a gradient boosting classifier on the provided tabular dataset using a time-based split and optimize for PR-AUC.

Frequently Asked Questions about train-evaluate-ml-model

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

FAQPage Schema
How do I prevent data leakage when training machine learning models with scikit-learn?▼

To prevent data leakage during machine learning model training, implement rigorous Pipeline-based workflows that fit feature engineering and scaling exclusively on training data before validating.

What is the best way to evaluate imbalanced classification models for business use cases?▼

Evaluating imbalanced classification models requires business-aligned metric selection, such as optimizing for PR-AUC instead of accuracy to reflect real-world costs in fraud detection scenarios.

How do I set up cross-validation for predictive modeling on grouped or temporal tabular data?▼

Cross-validation for predictive modeling on temporal or grouped tabular data requires specialized splitting protocols to avoid future-leaking, ensuring validation strategies respect entity boundaries.

Can I use gradient boosting libraries for regression and clustering tasks with this workflow?▼

Gradient boosting libraries support classification, regression, and clustering tasks within reproducible, experiment-tracked workflows that diagnose bias-variance tradeoffs on tabular datasets.

Why does my machine learning model overfit despite using cross-validation protocols?▼

Machine learning models overfit during cross-validation if feature engineering is not strictly isolated within pipelines, causing data leakage between training folds and validation sets.

Do I need scikit-learn to execute reproducible machine learning modeling workflows?▼

Scikit-learn is required to execute reproducible machine learning modeling workflows, providing the Pipeline architecture necessary for leakage-safe training and cross-validation protocols.

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