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[SPARK-23127][DOC] Update FeatureHasher guide for categoricalCols parameter #20293

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[SPARK-23127][DOC] Update FeatureHasher guide for categoricalCols parameter #20293

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6 changes: 3 additions & 3 deletions docs/ml-features.md
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Expand Up @@ -222,9 +222,9 @@ The `FeatureHasher` transformer operates on multiple columns. Each column may co
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.
feature value to its index in the feature vector. By default, numeric features are not treated
as categorical (even when they are integers). To treat them as categorical, specify the relevant
columns using the `categoricalCols` parameter.
- 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](ml-features.html#onehotencoder) with
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