XGBOOST

Extreme Gradient Boosting belongs to a family of boosting algorithms and uses the gradient boosting (GBM) framework. It is called extreme gradient boosting because it uses a gradient descent algorithm to minimize the loss when adding new models. So what makes it fast is its capacity to do parallel computation on a single machine. This makes XGBoost at least 10 times faster than existing gradient boosting implementations. XGBoost is a scalable and accurate implementation of gradient boosting machines and it has proven to push the limits of computing power for boosted trees algorithms as it was built and developed for the sole purpose of model performance and computational speed. To begin training, a single decision tree is built to predict the label.