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LeandroBenjaminL
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ml-modeling

Train machine learning models with validation and explainability using scikit-learn, lightgbm, xgboost, and shap.

Updated May 9, 2026
Search Tags:#machine learning#model training#model evaluation#explainability#robust validation
One-click install
npx skills add https://github.com/LeandroBenjaminL/lend-ai --skill ml-modeling
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ml-modeling
Source: https://github.com/LeandroBenjaminL/lend-ai/tree/main/skills/ml-modeling
Command: npx skills add https://github.com/LeandroBenjaminL/lend-ai --skill ml-modeling

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scikit-learn, lightgbm, xgboost, shap, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a comprehensive approach to machine learning, from simple baseline models to production-ready models with robust validation and explainability.

Core Features & Use Cases

  • Baseline First: Evaluates simple models before moving to more complex ones.
  • Validation: Utilizes Stratified K-Fold Cross Validation for robust model validation.
  • Desbalanceo Handling: Adapts to imbalanced datasets using F1-Score, Precision-Recall, AUC-ROC.
  • Feature Leakage: Ensures no feature leaks information from the future.
  • Model Offering: Provides interpretable models, ensembles, and auto-tuning for optimal performance.
  • Use Case: When you need to train models, select algorithms, evaluate metrics, or make predictions.

Quick Start

Use the ml-modeling skill to train a machine learning model on your dataset 'data.csv'.

Frequently Asked Questions about ml-modeling

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

FAQPage Schema
How do I train a machine learning model with robust validation?▼

Machine learning model training with robust validation requires Stratified K-Fold Cross Validation to ensure stable performance across data splits. This Skill automates that pipeline, evaluating baseline models first before advancing to complex ensembles and auto-tuning.

What's the best way to handle imbalanced datasets during model evaluation?▼

Imbalanced datasets during model evaluation are handled using F1-Score, Precision-Recall, and AUC-ROC metrics. This Skill adapts validation metrics specifically for desbalanceo handling, ensuring reliable performance measurement on skewed target distributions.

How does SHAP explainability work with trained models?▼

SHAP explainability works with trained models by calculating feature importance to interpret individual predictions. This Skill integrates SHAP to provide explainability for the trained machine learning models, clarifying feature contributions for analysis.

Can I use LightGBM and XGBoost for model selection?▼

Yes, LightGBM and XGBoost can be used for model selection. This Skill includes interpretable models, ensembles, and auto-tuning, utilizing scikit-learn, LightGBM, and XGBoost to evaluate and select the optimal algorithm for your dataset.

How do I prevent feature leakage in machine learning pipelines?▼

Preventing feature leakage in machine learning pipelines involves ensuring no feature leaks information from the future. This Skill explicitly checks for feature leakage during training to maintain valid model validation and prevent inflated metrics.

Do I need scikit-learn to train baseline models?▼

Yes, scikit-learn is required to train baseline models. This Skill needs scikit-learn, LightGBM, XGBoost, and SHAP installed to execute its machine learning training, model selection, and explainability workflows.

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