Final answer:
XGBoost is an optimization of gradient boosting for speed and performance. It sequentially builds decision trees to minimize a loss function, using regularization to prevent overfitting and handling missing data. Its features include powerful regularization, handling missing values, support for multiple data types, built-in cross-validation, and parallel processing.
Step-by-step explanation:
XGBoost (eXtreme Gradient Boosting) is an advanced implementation of gradient boosting that is designed to bemore efficient, flexible, and portable. It's an algorithm that has become popular among data scientists and machine learning practitioners for its performance in a variety of machine learning competitions and tasks.improves upon the concept of gradient boosting by using more sophisticated regularization (L1 and L2), which prevents overfitting, and by employing a novel tree learning algorithm. The core principle behind XGBoost is to build an ensemble of decision trees in a sequential manner.
Each new tree attempts to correct the errors made by the previous trees. To be more specific, XGBoost adds trees that predict the residuals or errors of prior trees multiplied by a learning rate. This process is repeated until the loss function (which evaluates the model's accuracy) can no longer be improved significantly.Note: A diagram is usually helpful to better understand the structure and flow of decision trees created by XGBoost, but in this format, we can't display images. However, you can imagine a flowchart where nodes represent decision checkpoints that split the data based on certain features and leaf nodes represent the final output of the tree which aggregates to form the model's prediction.