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 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.
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 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.
Note: This ignores instance weights (setting all to 1.0) from LogisticRegression.weightCol. This will change in later Spark versions.
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.