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.
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 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.
dataframe outputted by the model's transform
method.
dataframe outputted by the model's transform
method.
field in "predictions" which gives the calibrated probability of each instance.
field in "predictions" which gives the calibrated probability of each instance.
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
:: Experimental :: Binary Logistic regression results for a given model.