I'm trying to obtain ROC Curve for GBTClassifier.
One way is to reuse BinaryClassificationMetrics, however the path given in the documentation (https://spark.apache.org/docs/latest/mllib-evaluation-metrics.html) provides only 4 values for the ROC Curve, like:
[0.0|0.0]
[0.0|0.9285714285714286]
[1.0|1.0]
[1.0|1.0]
Another way is to use the "probability" column instead of "prediction". However, in case of GBTClassifier I don't have it and this solution works mostly for RandomForestClassifier.
How to plot ROC curve and precision-recall curve from BinaryClassificationMetrics
So what is the general/common way to get a ROC curve with enough points for an arbitrary classifier?