how interacting variables (known in statistics as moderating variable) are handled by KNN algorithm?

Can someone intuitively explain how interacting variables are being handled by KNN.according to the book Introduction to Data Mining:

Nearest neighbor classifiers can handle the presence of interacting attributes, i.e., attributes that have more predictive power taken in combination then by themselves, by using appropriate proximity measures that can incorporate the effects of multiple attributes together.

is there an example to help to demonstrate it?

thank you

Topic feature-interaction k-nn

Category Data Science

1
answered at

Interaction effect means the target is dependent on the interaction of two features i.e. X,Y but the data doesn't consider that Feature i.e. XY.

Simple model which tries to find a global pattern with the dataset will suffer from this issue e.g. LinearRegression.
Interaction Effect needs a full chapter for itself. Read here

Let's check this plot where the target is dependent on (X, Y, XY). For simplicity, I have kept the plot 2-D (ignoring Y).
We can easily observe that LinearRegression will have more challenges to predict the circled point.

While KNN will better approximate since it is not dependent on global pattern and will look on nearby data.

$\hspace{3cm}$enter image description here

In other words, the Interaction effect doesn't look prominent in a small portion of the space while it can have a large impact if we see the whole dataset together.
KNN works only with a small portion of space at a time(assuming small K).

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