Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/19024#discussion_r135307551
--- Diff: docs/ml-features.md ---
@@ -53,9 +53,9 @@ are calculated based on the mapped indices. This approach
avoids the need to com
term-to-index map, which can be expensive for a large corpus, but it
suffers from potential hash
collisions, where different raw features may become the same term after
hashing. To reduce the
chance of collision, we can increase the target feature dimension, i.e.
the number of buckets
-of the hash table. Since a simple modulo is used to transform the hash
function to a column index,
+of the hash table. Since a simple modulo is used to transform the hash
function to a vector index,
it is advisable to use a power of two as the feature dimension, otherwise
the features will
-not be mapped evenly to the columns. The default feature dimension is
`$2^{18} = 262,144$`.
+not be mapped evenly in the vector. The default feature dimension is
`$2^{18} = 262,144$`.
--- End diff --
yeah that's better
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