skills.rest
hung-phan
hung-phanCOMMUNITY
Explore all skills by this author →
GitHub

gradient-boosting

Guide selection and tuning of XGBoost, LightGBM, and CatBoost models for tabular data.

1|Updated Jun 4, 2026
Search Tags:#LightGBM#XGBoost#CatBoost#tabular data#gradient boosting
One-click install
npx skills add https://github.com/hung-phan/ml-skills --skill gradient-boosting
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gradient-boosting
Source: https://github.com/hung-phan/ml-skills/tree/main/skills/ml-review/references/ml-libraries/xgboost
Command: npx skills add https://github.com/hung-phan/ml-skills --skill gradient-boosting

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires xgboost, lightgbm, catboost, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenges of modeling tabular data by providing insights into the strengths and applications of XGBoost, LightGBM, and CatBoost, three popular gradient boosting frameworks.

Core Features & Use Cases

  • Hyperparameter Tuning: Offers guidance on tuning learning rate, depth, and regularization for optimal model performance.
  • Feature Importance: Provides methods to understand the impact of different features on the model.
  • GPU Training: Supports GPU-based training for faster computation on large datasets.
  • Use Case: When you need to build a robust model for structured data, this Skill can help you choose the right gradient boosting framework and optimize its parameters.

Quick Start

Train a gradient boosting model on your dataset using XGBoost with the following command: xgb.XGBClassifier(n_estimators=100, max_depth=3, learning_rate=0.1).fit(X_train, y_train).

Frequently Asked Questions about gradient-boosting

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

FAQPage Schema
How do I choose between XGBoost, LightGBM, and CatBoost for tabular data modeling?▼

Choosing between XGBoost, LightGBM, and CatBoost for tabular data modeling depends on dataset characteristics and framework strengths, with this Skill providing insights to select the right gradient boosting framework for your structured data tasks.

What is the best way to tune hyperparameters for gradient boosting models like XGBoost?▼

The best way to tune hyperparameters for gradient boosting models involves adjusting learning rate, depth, and regularization, with this Skill offering specific guidance to optimize these parameters for optimal model performance.

How do I extract feature importance from LightGBM or CatBoost models?▼

Extracting feature importance from LightGBM or CatBoost models helps understand the impact of different features on the model, with this Skill providing methods to evaluate feature impact for structured data modeling.

Can I use GPU training with XGBoost and LightGBM for large datasets?▼

Yes, you can use GPU training with XGBoost and LightGBM for large datasets, as this Skill supports GPU-based training to enable faster computation during gradient boosting model training.

Do I need prior machine learning knowledge to use this gradient boosting Skill?▼

Yes, you need prior knowledge of XGBoost, LightGBM, and CatBoost for model training and hyperparameter tuning, as this Skill targets data scientists and ML engineers working on structured data modeling tasks.

Related Skills

Explore More Data & Analytics Skills→
olavocarvalhoolavocarvalho
1

tabular-ml-modeling

Develops GPU-accelerated gradient boosting models for large-scale tabular data and classification tasks.

Community
Advanced
tondevreltondevrel
19

xgboost-lightgbm

Train XGBoost and LightGBM models for classification and regression on tabular data.

Community
Intermediate
KameniAlexNeaKameniAlexNea

hpo

Run Bayesian hyperparameter optimization for LightGBM, XGBoost, and CatBoost models with Optuna.

Community
Intermediate
Lord1EgyptLord1Egypt
2

arboreto

Infer gene regulatory networks from expression data using gradient boosting and random forests.

Community
Advanced