Github user MechCoder commented on a diff in the pull request:
https://github.com/apache/spark/pull/8197#discussion_r37336156
--- 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 --
Actually the cast will not change. Even when multiclass classification is
supported, we will have to do an explicit cast since the binary metrics will
not be available in the sealed trait. Only the multiclass metrics which will
not raise errors for binary data will be common to both.
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