CART – Distinguishing between majority and pure labels

[Rose W]

In the CART model, is there any way of distinguishing between pure labels and majority labels to help you work out the likelihood that the unlabelled data will be classified correctly?

[Simon]

I think you are asking about the distribution of labels at a leaf of the tree – is that what you mean?

In general, with real data, we will not get pure leaves (i.e., all data points have a single label). So, we can say that there is always a distribution of labels at every leaf.

The question then becomes: how do we make use of that, when making predictions for unseen test data points? There are two possibilities:

1. give the test data point the majority label of the leaf that it reaches
2. give the test data point a probabilistic label (i.e., a distribution across all possible label values) of the leaf that it reaches

In the second case, some subsequent process will have to resolve the uncertainty about the label – perhaps by using additional information such as the sequence of labels assigned to preceding and following points (in a sequence).

[Author]

Posts