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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