Github user feynmanliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/8197#discussion_r37349237
--- 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 --
Synced with @jkbradley offline. Summary:
We should not require end users to perform any sort of downcasting in the
stabilized API. This is OK for now since the API is still experimental.
Eventually we could provide two methods, a `summary :
LogisticRegressionSummary` and a `binarySummary :
BInaryLogisticRegressionSummary` which errors when called on a multiclass
LRModel. This will be easy to implement because `summary` is returning the base
`LogisticRegressionSummary` class so will not require any public API change.
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