Gradient Boosting is another technique for performing supervised machine learning tasks, like classification and regression. XGBoost stands for Extreme Gradient Boosting; it is a specific implementation of the Gradient Boosting method which uses more accurate approximations to find the best tree model. It employs a number of nifty tricks that make it exceptionally successful, particularly with structured data. Let's talk in more details.
A decision tree is a supervised machine learning model used to predict a target by learning decision rules from features. Classification and Regression Trees is a term to refer to Decision Tree algorithms that can be used for classification or regression predictive modelling problems. Let's talk in more details.
Decision trees are supervised learning models used for problems involving classification and regression. Tree models present a high flexibility that comes at a price: on one hand, trees are able to capture complex non-linear relationships; on the other hand, they are prone to memorizing the noise present in a dataset. Let's talk in more details.