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Decision Trees are a type of artificial intelligence (AI) that can be used to make decisions or predict outcomes based on data. They are built using a process called recursive partitioning, which involves splitting the data into subsets based on the most significant variables. Decision Trees have many practical applications, from fraud detection and credit scoring to medical diagnosis and risk assessment. They are easy to interpret and understand, and can handle both categorical and numerical data. However, Decision Trees can be prone to overfitting, which can result in poor performance on new, unseen data.