diff options
4 files changed, 10 insertions, 24 deletions
diff --git a/docs/mllib-evaluation-metrics.md b/docs/mllib-evaluation-metrics.md index a269dbf030..c49bc4ff12 100644 --- a/docs/mllib-evaluation-metrics.md +++ b/docs/mllib-evaluation-metrics.md @@ -140,7 +140,7 @@ definitions of positive and negative labels is straightforward. #### Label based metrics Opposed to binary classification where there are only two possible labels, multiclass classification problems have many -possible labels and so the concept of label-based metrics is introduced. Overall precision measures precision across all +possible labels and so the concept of label-based metrics is introduced. Accuracy measures precision across all labels - the number of times any class was predicted correctly (true positives) normalized by the number of data points. Precision by label considers only one class, and measures the number of time a specific label was predicted correctly normalized by the number of times that label appears in the output. @@ -182,21 +182,11 @@ $$\hat{\delta}(x) = \begin{cases}1 & \text{if $x = 0$}, \\ 0 & \text{otherwise}. </td> </tr> <tr> - <td>Overall Precision</td> - <td>$PPV = \frac{TP}{TP + FP} = \frac{1}{N}\sum_{i=0}^{N-1} \hat{\delta}\left(\hat{\mathbf{y}}_i - - \mathbf{y}_i\right)$</td> - </tr> - <tr> - <td>Overall Recall</td> - <td>$TPR = \frac{TP}{TP + FN} = \frac{1}{N}\sum_{i=0}^{N-1} \hat{\delta}\left(\hat{\mathbf{y}}_i - + <td>Accuracy</td> + <td>$ACC = \frac{TP}{TP + FP} = \frac{1}{N}\sum_{i=0}^{N-1} \hat{\delta}\left(\hat{\mathbf{y}}_i - \mathbf{y}_i\right)$</td> </tr> <tr> - <td>Overall F1-measure</td> - <td>$F1 = 2 \cdot \left(\frac{PPV \cdot TPR} - {PPV + TPR}\right)$</td> - </tr> - <tr> <td>Precision by label</td> <td>$PPV(\ell) = \frac{TP}{TP + FP} = \frac{\sum_{i=0}^{N-1} \hat{\delta}(\hat{\mathbf{y}}_i - \ell) \cdot \hat{\delta}(\mathbf{y}_i - \ell)} diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala index 0b84e0a3fa..794b1e7d9d 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala @@ -39,16 +39,16 @@ class MulticlassClassificationEvaluator @Since("1.5.0") (@Since("1.5.0") overrid def this() = this(Identifiable.randomUID("mcEval")) /** - * param for metric name in evaluation (supports `"f1"` (default), `"precision"`, `"recall"`, - * `"weightedPrecision"`, `"weightedRecall"`, `"accuracy"`) + * param for metric name in evaluation (supports `"f1"` (default), `"weightedPrecision"`, + * `"weightedRecall"`, `"accuracy"`) * @group param */ @Since("1.5.0") val metricName: Param[String] = { - val allowedParams = ParamValidators.inArray(Array("f1", "precision", - "recall", "weightedPrecision", "weightedRecall", "accuracy")) + val allowedParams = ParamValidators.inArray(Array("f1", "weightedPrecision", + "weightedRecall", "accuracy")) new Param(this, "metricName", "metric name in evaluation " + - "(f1|precision|recall|weightedPrecision|weightedRecall|accuracy)", allowedParams) + "(f1|weightedPrecision|weightedRecall|accuracy)", allowedParams) } /** @group getParam */ @@ -82,8 +82,6 @@ class MulticlassClassificationEvaluator @Since("1.5.0") (@Since("1.5.0") overrid val metrics = new MulticlassMetrics(predictionAndLabels) val metric = $(metricName) match { case "f1" => metrics.weightedFMeasure - case "precision" => metrics.accuracy - case "recall" => metrics.accuracy case "weightedPrecision" => metrics.weightedPrecision case "weightedRecall" => metrics.weightedRecall case "accuracy" => metrics.accuracy diff --git a/mllib/src/test/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluatorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluatorSuite.scala index 522f6675d7..1a3a8a13a2 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluatorSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluatorSuite.scala @@ -33,7 +33,7 @@ class MulticlassClassificationEvaluatorSuite val evaluator = new MulticlassClassificationEvaluator() .setPredictionCol("myPrediction") .setLabelCol("myLabel") - .setMetricName("recall") + .setMetricName("accuracy") testDefaultReadWrite(evaluator) } diff --git a/python/pyspark/ml/evaluation.py b/python/pyspark/ml/evaluation.py index b8b2b37af5..c480525e9b 100644 --- a/python/pyspark/ml/evaluation.py +++ b/python/pyspark/ml/evaluation.py @@ -258,9 +258,7 @@ class MulticlassClassificationEvaluator(JavaEvaluator, HasLabelCol, HasPredictio >>> evaluator = MulticlassClassificationEvaluator(predictionCol="prediction") >>> evaluator.evaluate(dataset) 0.66... - >>> evaluator.evaluate(dataset, {evaluator.metricName: "precision"}) - 0.66... - >>> evaluator.evaluate(dataset, {evaluator.metricName: "recall"}) + >>> evaluator.evaluate(dataset, {evaluator.metricName: "accuracy"}) 0.66... .. versionadded:: 1.5.0 |