Which decision tree algorithm supports multi-class classification?

When it comes to multi-class classification tasks, not all decision tree algorithms are created equal. Among the options provided, C4.5 stands out as the choice capable of handling such scenarios. Unlike ID3, which is primarily suited for binary classification, C4.5 extends its capabilities to handle multiple classes, making it a versatile tool in the realm of decision tree algorithms. While CART can also handle multi-class classification, it's notable that C4.5 specifically excels in this context. AdaBoost, on the other hand, isn't a decision tree algorithm per se; rather, it's a meta-algorithm that can boost the performance of decision trees, including C4.5, in classification tasks. Therefore, for multi-class classification scenarios, the best bet among the given options is undoubtedly C4.5. 🌟