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[Minor] [ML] [PySpark] ml.evaluation Scala and Python API sync #13195

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[Minor] [ML] [PySpark] ml.evaluation Scala and Python API sync #13195

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ml.evaluation Scala and Python API sync
yanboliang May 19, 2016
6b9fdde
fix typo
yanboliang May 19, 2016
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2 changes: 1 addition & 1 deletion mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala
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Expand Up @@ -28,7 +28,7 @@ import org.apache.spark.sql.types.DoubleType

/**
* :: Experimental ::
* Evaluator for multiclass classification, which expects two input columns: score and label.
* Evaluator for multiclass classification, which expects two input columns: prediction and label.
*/
@Since("1.5.0")
@Experimental
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5 changes: 1 addition & 4 deletions python/pyspark/ml/evaluation.py
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Expand Up @@ -193,9 +193,6 @@ class RegressionEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol):

.. versionadded:: 1.4.0
"""
# Because we will maximize evaluation value (ref: `CrossValidator`),
# when we evaluate a metric that is needed to minimize (e.g., `"rmse"`, `"mse"`, `"mae"`),
# we take and output the negative of this metric.
metricName = Param(Params._dummy(), "metricName",
"""metric name in evaluation - one of:
rmse - root mean squared error (default)
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@yanboliang yanboliang May 19, 2016

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In Spark 1.4, we negated some metrics from RegressionEvaluator since CrossValidator always maximized metrics. This was fixed in 1.5, #12377 updated the Scala docs, this PR updates the Python docs.

Expand Down Expand Up @@ -270,7 +267,7 @@ class MulticlassClassificationEvaluator(JavaEvaluator, HasLabelCol, HasPredictio
"""
metricName = Param(Params._dummy(), "metricName",
"metric name in evaluation "
"(f1|precision|recall|weightedPrecision|weightedRecall)",
"(f1|precision|recall|weightedPrecision|weightedRecall|accuracy)",
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@yanboliang yanboliang May 19, 2016

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Refer #12882 who add this metric.

typeConverter=TypeConverters.toString)

@keyword_only
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