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 = 1.0.
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.
field in "predictions" which gives the features of each instance as a vector.
field in "predictions" which gives the features of each instance as a vector.
field in "predictions" which gives the true label of each instance.
field in "predictions" which gives the true label of each instance.
Returns the precision-recall curve, which is a Dataframe containing two fields recall, precision with (0.0, 1.0) prepended to it.
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.
dataframe output by the model's transform
method.
dataframe output by the model's transform
method.
field in "predictions" which gives the probability of each class as a vector.
field in "predictions" which gives the probability of each class as a vector.
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 (0.0, 0.0) prepended and (1.0, 1.0) appended to it.
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.
:: Experimental :: Binary Logistic regression results for a given model.