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.
Note: This ignores instance weights (setting all to 1.0) from LogisticRegression.weightCol
.
This will change in later Spark versions.
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.
Note: This ignores instance weights (setting all to 1.0) from LogisticRegression.weightCol
.
This will change in later Spark versions.
objective function (scaled loss + regularization) at each iteration.
objective function (scaled loss + regularization) at each iteration.
Returns the precision-recall curve, which is a Dataframe containing two fields recall, precision with (0.
Returns the precision-recall curve, which is a Dataframe containing two fields recall, precision with (0.0, 1.0) prepended to it.
Note: This ignores instance weights (setting all to 1.0) from LogisticRegression.weightCol
.
This will change in later Spark versions.
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.
Note: This ignores instance weights (setting all to 1.0) from LogisticRegression.weightCol
.
This will change in later Spark versions.
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.
Note: This ignores instance weights (setting all to 1.0) from LogisticRegression.weightCol
.
This will change in later Spark versions.
Returns the receiver operating characteristic (ROC) curve, which is a Dataframe having two fields (FPR, TPR) with (0.
Returns the receiver operating characteristic (ROC) curve, which is a Dataframe having two fields (FPR, TPR) with (0.0, 0.0) prepended and (1.0, 1.0) appended to it. See http://en.wikipedia.org/wiki/Receiver_operating_characteristic
Note: This ignores instance weights (setting all to 1.0) from LogisticRegression.weightCol
.
This will change in later Spark versions.
Number of training iterations until termination
Number of training iterations until termination
:: Experimental :: Logistic regression training results.