Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/19024#discussion_r134741953
--- Diff: docs/ml-features.md ---
@@ -211,6 +211,65 @@ for more details on the API.
</div>
</div>
+## FeatureHasher
+
+Feature hashing projects a set of categorical or numerical features into a
feature vector of
+specified dimension (typically substantially smaller than that of the
original feature
+space). This is done using the [hashing
trick](https://en.wikipedia.org/wiki/Feature_hashing)
+to map features to indices in the feature vector.
+
+The `FeatureHasher` transformer operates on multiple columns. Each column
may contain either
+numeric or categorical features. Behavior and handling of column data
types is as follows:
+
+- Numeric columns: For numeric features, the hash value of the column name
is used to map the
+feature value to its index in the feature vector. Numeric features are
never treated as
+categorical, even when they are integers. You must explicitly convert
numeric columns containing
+categorical features to strings first.
+- String columns: For categorical features, the hash value of the string
"column_name=value"
+is used to map to the vector index, with an indicator value of `1.0`.
Thus, categorical features
+are "one-hot" encoded (similarly to using `OneHotEncoder` with
`dropLast=false`).
+- Boolean columns: Boolean values are treated in the same way as string
columns. That is,
+boolean features are represented as "column_name=true" or
"column_name=false", with an indicator
+value of `1.0`.
+
+Null (missing) values are ignored (implicitly zero in the resulting
feature vector).
+
+Since a simple modulo is used to transform the hash function to a vector
index,
--- End diff --
We should probably say something to the effect that the hashing mechanism
is the same as used for `HashingTF`
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