An ensemble machine learning technique that builds a prediction model in the form of an ensemble of weak prediction models, which are typically decision trees.

• Customer churn prediction
• Credit risk assessment
• Medical diagnosis
• Financial portfolio optimization
• Marketing campaign optimization

A company uses GBM to predict customer churn based on historical data. The weak learners are trained sequentially, with each one focusing on the errors of the previous ones to correct them. The final model is an ensemble of multiple decision trees that can accurately predict which customers are most likely to churn.

• Selecting an appropriate semantic modeling technique