diff --git a/docs/ml-guide.md b/docs/ml-guide.md index 44a316a07dfefc416e48167947d68378204a5fe4..1343753bce246bb6b74f7b7930baa1d187e2113a 100644 --- a/docs/ml-guide.md +++ b/docs/ml-guide.md @@ -628,7 +628,7 @@ Currently, `spark.ml` supports model selection using the [`CrossValidator`](api/ The `Evaluator` can be a [`RegressionEvaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.RegressionEvaluator) for regression problems, a [`BinaryClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.BinaryClassificationEvaluator) for binary data, or a [`MultiClassClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.MultiClassClassificationEvaluator) -for multiclass problems. The default metric used to choose the best `ParamMap` can be overriden by the `setMetric` +for multiclass problems. The default metric used to choose the best `ParamMap` can be overriden by the `setMetricName` method in each of these evaluators. The `ParamMap` which produces the best evaluation metric (averaged over the `$k$` folds) is selected as the best model. @@ -951,4 +951,4 @@ model.transform(test) {% endhighlight %} </div> -</div> \ No newline at end of file +</div> diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala index bfb70963b151d273ed56018b77085cfb8213902b..f71726f110e848081bc91da826415e03f3fca8fe 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala @@ -39,8 +39,7 @@ class BinaryClassificationEvaluator @Since("1.4.0") (@Since("1.4.0") override va def this() = this(Identifiable.randomUID("binEval")) /** - * param for metric name in evaluation - * Default: areaUnderROC + * param for metric name in evaluation (supports `"areaUnderROC"` (default), `"areaUnderPR"`) * @group param */ @Since("1.2.0")