BinaryLogisticRegressionSummary

java.lang.Object
- org.apache.spark.ml.classification.BinaryLogisticRegressionSummary

All Implemented Interfaces:

java.io.Serializable, LogisticRegressionSummary

Direct Known Subclasses:

BinaryLogisticRegressionTrainingSummary
```
public class BinaryLogisticRegressionSummary
extends java.lang.Object
implements LogisticRegressionSummary
```
:: Experimental :: Binary Logistic regression results for a given model.
param: predictions dataframe output by the model's transform method. param: probabilityCol field in "predictions" which gives the probability of each class as a vector. param: labelCol field in "predictions" which gives the true label of each instance. param: featuresCol field in "predictions" which gives the features of each instance as a vector.

See Also:
Serialized Form

Method Summary

Methods
Modifier and Type	Method and Description
`double`	`areaUnderROC()` Computes the area under the receiver operating characteristic (ROC) curve.
`java.lang.String`	`featuresCol()` Field in "predictions" which gives the features of each instance as a vector.
`Dataset<Row>`	`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 true label of each instance (if available).
`Dataset<Row>`	`pr()` Returns the precision-recall curve, which is an Dataframe containing two fields recall, precision with (0.0, 1.0) prepended to it.
`Dataset<Row>`	`precisionByThreshold()` Returns a dataframe with two fields (threshold, precision) curve.
`Dataset<Row>`	`predictions()` Dataframe output by the model's `transform` method.
`java.lang.String`	`probabilityCol()` Field in "predictions" which gives the probability of each class as a vector.
`Dataset<Row>`	`recallByThreshold()` Returns a dataframe with two fields (threshold, recall) curve.
`Dataset<Row>`	`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.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Method Detail
  - predictions
```
public Dataset<Row> predictions()
```
    Description copied from interface: LogisticRegressionSummary
    
    Dataframe output by the model's `transform` method.
    
    Specified by:
    
    predictions in interface LogisticRegressionSummary
  - probabilityCol
```
public java.lang.String probabilityCol()
```
    Description copied from interface: LogisticRegressionSummary
    
    Field in "predictions" which gives the probability of each class as a vector.
    
    Specified by:
    
    probabilityCol in interface LogisticRegressionSummary
  - labelCol
```
public java.lang.String labelCol()
```
    Description copied from interface: LogisticRegressionSummary
    
    Field in "predictions" which gives the true label of each instance (if available).
    
    Specified by:
    
    labelCol in interface LogisticRegressionSummary
  - featuresCol
```
public java.lang.String featuresCol()
```
    Description copied from interface: LogisticRegressionSummary
    
    Field in "predictions" which gives the features of each instance as a vector.
    
    Specified by:
    
    featuresCol in interface LogisticRegressionSummary
  - roc
```
public Dataset<Row> 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.
    Note: This ignores instance weights (setting all to 1.0) from LogisticRegression.weightCol. This will change in later Spark versions.
    
    Returns:
    (undocumented)
    See Also:
    http://en.wikipedia.org/wiki/Receiver_operating_characteristic
  - areaUnderROC
```
public double areaUnderROC()
```
    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:
    (undocumented)
  - pr
```
public Dataset<Row> pr()
```
    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:
    (undocumented)
  - fMeasureByThreshold
```
public Dataset<Row> fMeasureByThreshold()
```
    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.
    
    Returns:
    (undocumented)
  - precisionByThreshold
```
public Dataset<Row> precisionByThreshold()
```
    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:
    (undocumented)
  - recallByThreshold
```
public Dataset<Row> recallByThreshold()
```
    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:
    (undocumented)

Class BinaryLogisticRegressionSummary

Method Summary

Methods inherited from class java.lang.Object

Method Detail

predictions

probabilityCol

labelCol

featuresCol

roc

areaUnderROC

pr

fMeasureByThreshold

precisionByThreshold

recallByThreshold