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I'm reading this paper that establishes SHAP values for Machine Learning explainability.

On page 2 of the paper they put some set builder notation to an exponent $M$, as below:

$$z' \in \{1, 0\}^M$$

I presume this means that $z'$ is a vector of size $M$, where each index takes the value $0$ or $1$.

Is this the correct way to interpret this notation?

Connor
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1 Answers1

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Yes, since $M$ is a natural number ("the number of simplified input features"). In the paper, this is in the context of a definition $$ g(z') = \phi_0 + \sum_{i=1}^M \phi_iz'_i $$ where $z'$ is being indexed by $i$ for $1 \le i \le M$. The calculation is adding up $\phi_0$ and some of the other $\phi_i$ values, with $z'$ telling us which ones to include in the total.

More generally, people write $B^A$ for the set of functions from $A$ to $B$. If you like, you can think of $\{0,1\}^M$ as meaning the same as $\{0,1\}^{\{1, 2, \dots, M\}}$, which is the set of functions that take in a number between $1$ and $M$, and spit out either $0$ or $1$. If $f$ is a function like that, then you can make a vector of $0$s and $1$s: $$ (f(1), f(2), f(3), \dots, f(M)). $$ And also, if you had a vector of that length, you could use it to define a function, just by indexing into the vector. So the function and the vector are really two different views of the same data. Mathematicians often switch freely between these viewpoints.

alexg
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