XGBoost

XGBoost is an optimized distributed gradient boosting library designed for high efficiency, speed, and predictive accuracy on tabular datasets.

When to Use

• Tabular Data Competitions & Industry Production: High-performance gradient boosted decision trees for classification and regression.
• GPU-Accelerated Tree Building: Training massive datasets on NVIDIA GPUs with tree_method="hist" and device="cuda".
• Handling Missing Values & Sparsity: Automatic direction assignment for missing values without manual imputation.
• Monotonic Feature Constraints: Enforcing domain-specific rules (e.g., higher credit score must never increase interest rate).

Quick Start

import xgboost as xgb
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split