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-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org