Github user MLnick commented on a diff in the pull request: https://github.com/apache/spark/pull/19024#discussion_r135261228 --- 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 -- "to vector indices" perhaps?
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