Hi guys, This may be a stupid question. But I m facing an issue here.
I found the class BinaryClassificationMetrics and I wanted to compute the aucROC or aucPR of my model. The thing is that the predict method of a LogisticRegressionModel only returns the predicted class, and not the probability of belonging to the positive class. So I will get: val metrics = new BinaryClassificationMetrics(predictionAndLabels) val aucROC = metrics.areaUnderROC with predictionAndLabels as a RDD[(predictedClass,label)]. Here, because the predicted class will always be 0 or 1, there is no way to vary the threshold to get the aucROC, right ???? Or am I totally wrong ? So, is it relevant to use BinaryClassificationMetrics.areUnderROC with MLlib's classification models which in many cases only return the predicted class and not the probability ? Nevertheless, an easy solution for LogisticRegression would be to create my own method who takes the weights' vector of the model as a parameter and computes a predictionAndLabels with the real belonging probabilities. But is this the only solution ???? Thanks in advance. Regards, Jean. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Is-it-relevant-to-use-BinaryClassificationMetrics-aucROC-aucPR-with-LogisticRegressionModel-tp25465.html Sent from the Apache Spark User List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
