Skip to content

Navigation Menu

Sign in
Appearance settings

Search code, repositories, users, issues, pull requests...

Provide feedback

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly
Appearance settings

docs: update ML overview API doc #105

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鈥檒l occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Oct 13, 2023
Merged

docs: update ML overview API doc #105

Changes from all commits
Commits
Show all changes
1 commit
Select commit
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
12 changes: 10 additions & 2 deletions 12 README.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -95,10 +95,18 @@ using the
and the `bigframes.ml.compose module <https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.compose>`_.
BigQuery DataFrames offers the following transformations:

* Use the `OneHotEncoder class <https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.preprocessing.OneHotEncoder>`_
in the ``bigframes.ml.preprocessing`` module to transform categorical values into numeric format.
* Use the `KBinsDiscretizer class <https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.compose.ColumnTransformer>`_
in the ``bigframes.ml.preprocessing`` module to bin continuous data into intervals.
* Use the `LabelEncoder class <https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.preprocessing.LabelEncoder>`_
in the ``bigframes.ml.preprocessing`` module to normalize the target labels as integer values.
* Use the `MaxAbsScaler class <https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.preprocessing.MaxAbsScaler>`_
in the ``bigframes.ml.preprocessing`` module to scale each feature to the range ``[-1, 1]`` by its maximum absolute value.
* Use the `MinMaxScaler class <https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.preprocessing.MinMaxScaler>`_
in the ``bigframes.ml.preprocessing`` module to standardize features by scaling each feature to the range ``[0, 1]``.
* Use the `StandardScaler class <https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.preprocessing.StandardScaler>`_
in the ``bigframes.ml.preprocessing`` module to standardize features by removing the mean and scaling to unit variance.
* Use the `OneHotEncoder class <https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.preprocessing.OneHotEncoder>`_
in the ``bigframes.ml.preprocessing`` module to transform categorical values into numeric format.
* Use the `ColumnTransformer class <https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.compose.ColumnTransformer>`_
in the ``bigframes.ml.compose`` module to apply transformers to DataFrames columns.

Expand Down
Morty Proxy This is a proxified and sanitized view of the page, visit original site.