Github user feynmanliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/8197#discussion_r37345587
--- Diff: docs/ml-linear-methods.md ---
@@ -118,12 +133,114 @@ lrModel = lr.fit(training)
print("Weights: " + str(lrModel.weights))
print("Intercept: " + str(lrModel.intercept))
{% endhighlight %}
+</div>
</div>
+The `spark.ml` implementation of logistic regression also supports
+extracting a summary of the model over the training set. Note that the
+predictions and metrics which are stored as `Datafram`s in
+`BinaryLogisticRegressionSummary` are annoted `@transient` and hence
+only available on the driver.
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+
+[`LogisticRegressionTrainingSummary`](api/scala/index.html#org.apache.spark.ml.classification.LogisticRegressionTrainingSummary)
+provides a summary for a
+[`LogisticRegressionModel`](api/scala/index.html#org.apache.spark.ml.classification.LogisticRegressionModel).
+Currently, only binary classification is supported and the
+summary must be explicitly cast to
+[`BinaryLogisticRegressionTrainingSummary`](api/scala/index.html#org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary).
+This will likely change when multiclass classification is supported.
--- End diff --
Downcasting is almost always an indication of a poor abstraction and IMO
the stabilized API should not require any explicit typecasting by the end user,
[here's an
explanation](http://codebetter.com/jeremymiller/2006/12/26/downcasting-is-a-code-smell/)
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