public class BinaryLogisticRegressionSummary extends java.lang.Object implements LogisticRegressionSummary
transform
method.
param: probabilityCol field in "predictions" which gives the calibrated probability of
each sample.
param: labelCol field in "predictions" which gives the true label of each sample.Modifier and Type | Method and Description |
---|---|
double |
areaUnderROC()
Computes the area under the receiver operating characteristic (ROC) curve.
|
DataFrame |
fMeasureByThreshold()
Returns a dataframe with two fields (threshold, F-Measure) curve with beta = 1.0.
|
java.lang.String |
labelCol()
Field in "predictions" which gives the the true label of each sample.
|
DataFrame |
pr()
Returns the precision-recall curve, which is an Dataframe containing
two fields recall, precision with (0.0, 1.0) prepended to it.
|
DataFrame |
precisionByThreshold()
Returns a dataframe with two fields (threshold, precision) curve.
|
DataFrame |
predictions()
Dataframe outputted by the model's `transform` method.
|
java.lang.String |
probabilityCol()
Field in "predictions" which gives the calibrated probability of each sample as a vector.
|
DataFrame |
recallByThreshold()
Returns a dataframe with two fields (threshold, recall) curve.
|
DataFrame |
roc()
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.
|
public DataFrame predictions()
LogisticRegressionSummary
predictions
in interface LogisticRegressionSummary
public java.lang.String probabilityCol()
LogisticRegressionSummary
probabilityCol
in interface LogisticRegressionSummary
public java.lang.String labelCol()
LogisticRegressionSummary
labelCol
in interface LogisticRegressionSummary
public DataFrame roc()
http://en.wikipedia.org/wiki/Receiver_operating_characteristic
public double areaUnderROC()
public DataFrame pr()
public DataFrame fMeasureByThreshold()
public DataFrame precisionByThreshold()
public DataFrame recallByThreshold()