XGBoost
XGBoost (Extreme Gradient Boosting) is an open-source, highly optimized implementation of the gradient boosting algorithm, widely used for structured/tabular data problems due to its speed, scalability, and predictive accuracy.
7 resources across 2 libraries
Glossary Terms(5)
Ensemble Learning
Ensemble learning is a machine learning approach that combines the predictions of multiple individual models to produce a single, typically more accurate and r…
Random Forest
Random Forest is an ensemble learning algorithm that builds many decision trees on randomly resampled data and random feature subsets, then aggregates their pr…
Gradient Boosting
Gradient Boosting is an ensemble learning technique that builds a model as a sequence of weak learners, typically shallow decision trees, where each new learne…
XGBoost
XGBoost (Extreme Gradient Boosting) is an open-source, highly optimized implementation of the gradient boosting algorithm, widely used for structured/tabular d…
Decision Tree
A decision tree is a supervised learning model that predicts an outcome by learning a hierarchy of simple, interpretable if-then decision rules based on input…
Cheat Sheets(2)
XGBoost Cheat Sheet
XGBoost cheat sheet covering the scikit-learn and native training APIs, key hyperparameters, early stopping, and feature importance.
Gradient Boosting Cheat Sheet
A reference for gradient boosting covering XGBoost, LightGBM, and scikit-learn implementations, plus learning rate tuning, early stopping, and regularization.