How does supervised classification differ from unsupervised classification?

Supervised classification is characterized by its reliance on user-defined training data to guide the classification process. In this approach, the user selects specific classes of interest and provides representative samples of each class, which the algorithm uses to learn how to classify the remaining data. This results in a more controlled and targeted classification that can lead to higher accuracy, especially when sufficient and representative training data is used.

In contrast, unsupervised classification operates without predefined class labels or user input regarding the characteristics of the data. Instead, it attempts to identify inherent patterns or groupings within the dataset based on the characteristics of the data itself. This means it organizes the data into clusters without prior knowledge, making it fully automated in that sense.

The difference in these methodologies can lead to distinct outcomes in accuracy and specificity, with supervised classification usually yielding better results when appropriate training data is available. This is why the statement about supervised classification being user-defined and unsupervised being automated accurately captures the essence of how these two classification processes differ.