BinaryLogisticRegressionSummary (Spark 2.0.2 JavaDoc)

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

All Implemented Interfaces:

java.io.Serializable, LogisticRegressionSummary

Direct Known Subclasses:

BinaryLogisticRegressionTrainingSummary
```
public class BinaryLogisticRegressionSummary
extends 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.
`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.
`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 a 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.
`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 a Dataframe having two fields (FPR, TPR) with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.

Methods inherited from class Object
equals, 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 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 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 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 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.
    
    Returns:
    (undocumented)
  - 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 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:
    (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 Object

Method Detail

predictions

probabilityCol

labelCol

featuresCol

roc

areaUnderROC

pr

fMeasureByThreshold

precisionByThreshold

recallByThreshold