Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/13139#discussion_r63890860
--- Diff: docs/ml-classification-regression.md ---
@@ -374,6 +374,197 @@ regression model and extracting model summary
statistics.
</div>
+## Generalized linear regression
+
+When working with data that has a relatively small number of features (<
4096), Spark's GeneralizedLinearRegression interface
+allows for flexible specification of [generalized linear
models](https://en.wikipedia.org/wiki/Generalized_linear_model) (GLMs) which
can be used for various types of
+prediction problems including linear regression, Poisson regression,
logistic regression, and others.
+
+Contrasted with linear regression where the output is assumed to follow a
Gaussian
+distribution, GLMs are specifications of linear models where the response
variable $Y_i$ may take on _any_
+distribution from the [exponential family of
distributions](https://en.wikipedia.org/wiki/Exponential_family).
--- End diff --
The familes and link function are documented in the table below. I could
move the table, or are you suggesting something somewhat different?
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