XGBoost MLM Flashcards
XGBoost, short for eXtreme Gradient Boosting
XGBoost, short for eXtreme Gradient Boosting, is an optimized distributed gradient boosting library that provides a highly efficient, flexible, and portable solution for machine learning tasks.
- Introduction
XGBoost is a decision-tree-based ensemble machine learning algorithm that uses a gradient boosting framework. It is renowned for its execution speed and model performance, and it has been a go-to choice for many winning teams of machine learning competitions.
- Gradient Boosting
Gradient Boosting is a technique that produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. It builds the model in a stage-wise fashion and it generalizes them by allowing optimization of an arbitrary differentiable loss function.
- Speed and Performance
XGBoost is recognized for its speed and performance. The core XGBoost algorithm is parallelizable, which means it can harness all of the processing power of modern multi-core computers. Furthermore, it is also capable of being distributed across networks of computers to handle larger datasets.
- Regularization
XGBoost has an in-built regularization which helps to reduce overfitting. In fact, XGBoost is also known as a ‘regularized boosting’ technique.
- Handling Missing Values
XGBoost has an in-built routine to handle missing values, which allows the user to choose a different split for handling missing values, and uses this to learn the best imputation value for missing values based on reduction in the loss function.
- Tree Pruning
XGBoost uses a more principled approach for controlling model complexity and preventing overfitting through its depth-first tree pruning strategy, where splits are chosen to optimize for the loss function and a specified maximum depth, while traditional gradient boosting methods use a greedy algorithm.
- Built-in Cross-Validation
XGBoost allows a user to run a cross-validation at each iteration of the boosting process, making it easy to get the exact optimum number of boosting iterations in a single run.
- Flexibility
XGBoost allows users to define custom optimization objectives and evaluation criteria, which adds a whole new dimension to the model as now you can solve for almost all types of problems.
- Strengths and Limitations
XGBoost performs well in many predictive tasks and is often a key component of winning entries in machine learning competitions. However, due to its complexity and flexibility, it requires careful tuning of the hyperparameters for best performance.
- Applications
XGBoost has been used successfully in many machine learning and data science competitions and has a wide range of applications in industries such as banking, e-commerce, and healthcare.