Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/13139#discussion_r64327019
--- Diff: docs/ml-classification-regression.md ---
@@ -374,6 +374,154 @@ regression model and extracting model summary
statistics.
</div>
+## Generalized linear regression
+
+Contrasted with linear regression where the output is assumed to follow a
Gaussian
+distribution, [generalized linear
models](https://en.wikipedia.org/wiki/Generalized_linear_model) (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).
+Spark's `GeneralizedLinearRegression` interface
+allows for flexible specification of GLMs which can be used for various
types of
+prediction problems including linear regression, Poisson regression,
logistic regression, and others.
+Currently in `spark.ml`, only a subset of the exponential family
distributions are supported and they are listed
+[below](#available-families).
+
+**NOTE**: Spark currently only supports up to 4096 features for GLM
models, and will throw an exception if this
+constraint is exceeded. See the [optimization section](#optimization) for
more details.
--- End diff --
Note that, for certain models, you can call LinearRegression or
LogisticRegression to use other solvers which support more features.
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