XGBoost and Random Forest with Bayesian Optimisation

Instead of only comparing XGBoost and Random Forest in this post we will try to explain how to use those two very popular approaches with Bayesian Optimisation and that are those models main pros and cons. XGBoost (XGB) and Random Forest (RF) both are ensemble learning methods and predict (classification or regression) by combining the outputs from individual decision trees (we assume tree-based XGB or RF). XGBoost build decision tree one each time. Each new tree corrects errors which were made by previously trained decision tree. At Addepto we use XGBoost models to solve anomaly detection problems e.g. in supervised learning approach.