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-rw-r--r--posts/2018-12-02-lime-shapley.md4
1 files changed, 2 insertions, 2 deletions
diff --git a/posts/2018-12-02-lime-shapley.md b/posts/2018-12-02-lime-shapley.md
index 1f1fe34..5ccf701 100644
--- a/posts/2018-12-02-lime-shapley.md
+++ b/posts/2018-12-02-lime-shapley.md
@@ -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.