Introducing
Classtree enhances hierarchical classification, outperforming traditional flat classifiers in fine grained classification scenarios.
Classtree works by recognizing that classes can be grouped into a hierarchy of superclasses and subclasses. This approach allows for more general predictions when specific details are uncertain, optimizing the balance between being correct and specific.
Classtree uses a threshold to vary the prediction's granularity, enabling a classifier to make confident guesses at a higher level in the hierarchy even if it's unsure about finer details. This strategy is particularly useful when exact leaf-node predictions are challenging, offering a flexible way to navigate through the class hierarchy based on confidence levels.