[SPARK-15617][ML][DOC] Clarify that fMeasure in MulticlassMetrics is …

…"micro" f1_score

## What changes were proposed in this pull request?
1, del precision,recall in  `ml.MulticlassClassificationEvaluator`
2, update user guide for `mlllib.weightedFMeasure`

## How was this patch tested?
local build

Author: Ruifeng Zheng <ruifengz@foxmail.com>

Closes apache#13390 from zhengruifeng/clarify_f1.

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.
@@ -182,20 +182,10 @@ $$\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} =

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

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)
   }
 

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