Skip to content

[SPARK-23048][ML] Add OneHotEncoderEstimator document and examples #20257

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
wants to merge 6 commits into from
Closed

[SPARK-23048][ML] Add OneHotEncoderEstimator document and examples #20257

Changes from 1 commit
Commits
Show all changes
6 commits
Select commit Hold shift + click to select a range
21cb7d3
Update mllib docs for OneHotEncoderEstimator.
viirya Jan 13, 2018
13a7b90
Address comment.
viirya Jan 16, 2018
262c046
Remove OneHotEncoder examples.
viirya Jan 16, 2018
e57d9ee
Address comments.
viirya Jan 17, 2018
18cf226
Address comments.
viirya Jan 17, 2018
3c697bd
Add markdown link.
viirya Jan 17, 2018
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
6 changes: 3 additions & 3 deletions docs/ml-features.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -783,11 +783,11 @@ Because this existing `OneHotEncoder` is a stateless transformer, it is not usab

## OneHotEncoderEstimator

[One-hot encoding](http://en.wikipedia.org/wiki/One-hot) maps a column of label indices to a column of binary vectors, and each output binary vector includes at most a single one-value. This encoding allows algorithms which expect continuous features, such as Logistic Regression, to use categorical features. For string type input data, it is common to encode categorical features using [StringIndexer](ml-features.html#stringindexer) first.
[One-hot encoding](http://en.wikipedia.org/wiki/One-hot) maps a categorical feature, represented as a label index, to a binary vector with at most a single one-value indicating the presence of a specific feature value from among the set of all feature values.
Copy link
Contributor

@MLnick MLnick Jan 17, 2018

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

@viirya sorry for any confusion but I didn't intend you to remove these sentences:

This encoding allows algorithms which expect continuous features, such as Logistic Regression, to use categorical features. For string type input data, it is common to encode categorical features using [StringIndexer](ml-features.html#stringindexer) first.

Copy link
Member Author

@viirya viirya Jan 17, 2018

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

No problem. Added it back.


`OneHotEncoderEstimator` can transform multiple columns, returning a one-hot-encoded output vector column for each input column.
`OneHotEncoderEstimator` can transform multiple columns, returning an one-hot-encoded output vector column for each input column. It is common to merge these vectors into a single feature vector using `VectorAssembler`.
Copy link
Contributor

@MLnick MLnick Jan 17, 2018

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Add Markdown link for VectorAssembler


`OneHotEncoderEstimator` supports the `handleInvalid` parameter to choose how to handle invalid input during transforming data. Available options include 'keep' (any invalid inputs are assigned to an extra categorical number) and 'error' (throw an error).
`OneHotEncoderEstimator` supports the `handleInvalid` parameter to choose how to handle invalid input during transforming data. Available options include 'keep' (any invalid inputs are assigned to an extra categorical index) and 'error' (throw an error).

**Examples**

Expand Down