36.Which of the following combination is incorrect?  A. Continuous – euclidean distance  B. Continuous – correlation similarity  C. Binary – manhattan distance  D. You should choose a distance/similarity that makes sense for your problem.

The question seems to be incomplete as it does not provide the context or criteria for determining the correctness of the combinations. However, generally speaking, all the combinations A, B, and C can be used in different contexts.

A. Continuous variables can be compared using Euclidean distance. This is a common method in many machine learning algorithms.

B. Continuous variables can also be compared using correlation similarity. This is often used in statistics and data analysis to understand the relationship between two variables.

C. Binary variables can be compared using Manhattan distance. This is often used in computer science and information theory.

D. This is not a combination but a statement suggesting that the choice of distance or similarity measure should be appropriate for the specific problem at hand.

So, without additional context, it's not possible to definitively say which combination is incorrect.