Question

How might a non-stochastic decision tree be considered a black box algorithm?

A. If the algorithm is contained within an embedded system.
B. If the model is expressed in an annotated visual form.
C. If the algorithm serves as part of a data recorder.
D. If the model is far too complex to be readily understood by human beings.

Answer 1

A non-stochastic decision tree can be considered a black box algorithm when it becomes too complex for humans to understand its decision-making process.

Step-by-step explanation:

A non-stochastic decision tree can be considered a black box algorithm if the model is far too complex to be readily understood by human beings. Decision trees are commonly used in machine learning to make decisions or predictions based on input data. When a decision tree becomes very large and intricate, it becomes difficult for humans to comprehend how the model is making its decisions. This lack of interpretability is what gives rise to the term 'black box algorithm'.