org.apache.spark.ml.classification
Computes the area under the receiver operating characteristic (ROC) curve.
Computes the area under the receiver operating characteristic (ROC) curve.
Returns a dataframe with two fields (threshold, F-Measure) curve with beta = 1.
Returns a dataframe with two fields (threshold, F-Measure) curve with beta = 1.0.
objective function (scaled loss + regularization) at each iteration.
objective function (scaled loss + regularization) at each iteration.
Returns the precision-recall curve, which is an Dataframe containing two fields recall, precision with (0.
Returns the precision-recall curve, which is an Dataframe containing two fields recall, precision with (0.0, 1.0) prepended to it.
Returns a dataframe with two fields (threshold, precision) curve.
Returns a dataframe with two fields (threshold, precision) curve. Every possible probability obtained in transforming the dataset are used as thresholds used in calculating the precision.
Returns a dataframe with two fields (threshold, recall) curve.
Returns a dataframe with two fields (threshold, recall) curve. Every possible probability obtained in transforming the dataset are used as thresholds used in calculating the recall.
Returns the receiver operating characteristic (ROC) curve, which is an Dataframe having two fields (FPR, TPR) with (0.
Returns the receiver operating characteristic (ROC) curve, which is an Dataframe having two fields (FPR, TPR) with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.
http://en.wikipedia.org/wiki/Receiver_operating_characteristic
Number of training iterations until termination
Number of training iterations until termination
:: Experimental :: Logistic regression training results.