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@@ -76,8 +76,8 @@ feature contributions of supervised learning models locally.
Let $f: X_1 \times X_2 \times ... \times X_n \to \mathbb R$ be a
function. We can think of $f$ as a model, where $X_j$ is the space of
-$j$th feature. For example, in a language model, $X_j$ may be the count
-of the $j$th word in the vocabulary.
+$j$th feature. For example, in a language model, $X_j$ may correspond to
+the count of the $j$th word in the vocabulary.
The output may be something like housing price, or log-probability of
something.