Abstract
The classifiers discussed in the previous chapters expect all attribute values to be presented at the same time. Such a scenario, however, has its flaws. Thus a physician seeking to come to grips with the nature of her patient’s condition often has nothing to begin with save a few subjective symptoms. And so, to narrow the field of diagnoses, she prescribes lab tests, and, based on the results, perhaps other tests still. At any given moment, then, the doctor considers only “attributes” that promise to add meaningfully to her current information or understanding. It would be absurd to ask for all possible lab tests (thousands and thousands of them) right from the start.
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Notes
- 1.
Both spellings are used: leaves and leafs. The latter is probably more appropriate because the “leaf” in question is supposed to be a data abstraction that has nothing to do with the original physical object.
- 2.
In as sense, the decision tree can be seen as a simple mechanism for data compression.
- 3.
Recall that the relative frequency of pos is the percentage (in the training set) of examples labeled with pos; this represents the probability that a randomly drawn example will be positive.
- 4.
References
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Kubat, M. (2017). Decision Trees. In: An Introduction to Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-63913-0_6
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