[jira] [Created] (SPARK-17906) MulticlassClassificationEvaluator support target label

["zhengruifeng (JIRA)" <ji...@apache.org>]

zhengruifeng created SPARK-17906:
------------------------------------

             Summary: MulticlassClassificationEvaluator support target label
                 Key: SPARK-17906
                 URL: https://issues.apache.org/jira/browse/SPARK-17906
             Project: Spark
          Issue Type: Brainstorming
          Components: ML
            Reporter: zhengruifeng
            Priority: Minor


In practice, I sometime only focus metric of one special label.
For example, in CTR prediction, I usually only mind F1 of positive class.

In sklearn, this is supported:
{code}
>>> from sklearn.metrics import classification_report
>>> y_true = [0, 1, 2, 2, 2]
>>> y_pred = [0, 0, 2, 2, 1]
>>> target_names = ['class 0', 'class 1', 'class 2']
>>> print(classification_report(y_true, y_pred, target_names=target_names))
             precision    recall  f1-score   support

    class 0       0.50      1.00      0.67         1
    class 1       0.00      0.00      0.00         1
    class 2       1.00      0.67      0.80         3

avg / total       0.70      0.60      0.61         5
{code}

Now, ml only support `weightedXXX`. So I think there may be a point to improve.

The API may be designed like this:
{code}
val dataset = ...
val evaluator = new MulticlassClassificationEvaluator
evaluator.setMetricName("f1")
evaluator.evaluate(dataset)       // weightedF1 of all classes

evaluator.setTarget(0.0).setMetricName("f1")
evaluator.evaluate(dataset)       // F1 of class "0"
{code}


what's your opinion? [~yanboliang][~josephkb][~sethah][~srowen] 
If this is useful and acceptable, I'm happy to work on this. 



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org