about an item (represented in branches) to conclusions about the item’s target value (represented in leaves). Supports both binary and multiclass labels, as well as both continuous and categorical features. Extra Trees Classifier An averaging algorithm based on randomized decision trees. Gradient Boosted Tree Classifier Produces a classification prediction model in the form of an ensemble of decision trees. It only supports binary labels, as well as both continuous and categorical features. LGBM Classifier Gradient boosting framework that uses leaf-wise (horizontal) tree-based learning algorithm. Logistic Regression Analyzes a data set in which there are one or more independent variables that determine one of two outcomes. Only binary logistic regression is supported Random Forest Classifier Constructs multiple decision trees to produce the label that is a mode of each decision tree. It supports both binary and multiclass labels, as well as both continuous and categorical features. XGBoost Classifier Accurate sure procedure that can be used for classification problems. XGBoost models are used in a variety of areas including Web search ranking and ecology.