From e58223171ecae6450482aadf4e7994c3b8d8a58d Mon Sep 17 00:00:00 2001 From: Nick Pentreath Date: Fri, 19 Jan 2018 12:43:23 +0200 Subject: [PATCH] [SPARK-23127][DOC] Update FeatureHasher guide for categoricalCols parameter Update user guide entry for `FeatureHasher` to match the Scala / Python doc, to describe the `categoricalCols` parameter. ## How was this patch tested? Doc only Author: Nick Pentreath Closes #20293 from MLnick/SPARK-23127-catCol-userguide. (cherry picked from commit 60203fca6a605ad158184e1e0ce5187e144a3ea7) Signed-off-by: Nick Pentreath --- docs/ml-features.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/ml-features.md b/docs/ml-features.md index 72643137d96b1..10183c3e78c76 100644 --- a/docs/ml-features.md +++ b/docs/ml-features.md @@ -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