LogisticRegressionSummary (Spark 2.3.2 JavaDoc)

All Superinterfaces:

java.io.Serializable

All Known Subinterfaces:

BinaryLogisticRegressionSummary, BinaryLogisticRegressionTrainingSummary, LogisticRegressionTrainingSummary

All Known Implementing Classes:

BinaryLogisticRegressionSummaryImpl, BinaryLogisticRegressionTrainingSummaryImpl, LogisticRegressionSummaryImpl, LogisticRegressionTrainingSummaryImpl
```
public interface LogisticRegressionSummary
extends scala.Serializable
```
:: Experimental :: Abstraction for logistic regression results for a given model.
Currently, the summary ignores the instance weights.

Method Summary

All Methods Instance Methods Abstract Methods
Modifier and Type	Method and Description
`double`	`accuracy()` Returns accuracy.
`BinaryLogisticRegressionSummary`	`asBinary()` Convenient method for casting to binary logistic regression summary.
`double[]`	`falsePositiveRateByLabel()` Returns false positive rate for each label (category).
`String`	`featuresCol()` Field in "predictions" which gives the features of each instance as a vector.
`double[]`	`fMeasureByLabel()` Returns f1-measure for each label (category).
`double[]`	`fMeasureByLabel(double beta)` Returns f-measure for each label (category).
`String`	`labelCol()` Field in "predictions" which gives the true label of each instance (if available).
`double[]`	`labels()` Returns the sequence of labels in ascending order.
`MulticlassMetrics`	`multiclassMetrics()`
`double[]`	`precisionByLabel()` Returns precision for each label (category).
`String`	`predictionCol()` Field in "predictions" which gives the prediction of each class.
`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.
`double[]`	`recallByLabel()` Returns recall for each label (category).
`double[]`	`truePositiveRateByLabel()` Returns true positive rate for each label (category).
`double`	`weightedFalsePositiveRate()` Returns weighted false positive rate.
`double`	`weightedFMeasure()` Returns weighted averaged f1-measure.
`double`	`weightedFMeasure(double beta)` Returns weighted averaged f-measure.
`double`	`weightedPrecision()` Returns weighted averaged precision.
`double`	`weightedRecall()` Returns weighted averaged recall.
`double`	`weightedTruePositiveRate()` Returns weighted true positive rate.

- Method Detail
  - predictions
```
Dataset<Row> predictions()
```
    Dataframe output by the model's transform method.
    
    Returns:
    
    (undocumented)
  - probabilityCol
```
String probabilityCol()
```
    Field in "predictions" which gives the probability of each class as a vector.
  - predictionCol
```
String predictionCol()
```
    Field in "predictions" which gives the prediction of each class.
  - labelCol
```
String labelCol()
```
    Field in "predictions" which gives the true label of each instance (if available).
  - featuresCol
```
String featuresCol()
```
    Field in "predictions" which gives the features of each instance as a vector.
  - multiclassMetrics
```
MulticlassMetrics multiclassMetrics()
```
  - labels
```
double[] labels()
```
    Returns the sequence of labels in ascending order. This order matches the order used in metrics which are specified as arrays over labels, e.g., truePositiveRateByLabel.
    Note: In most cases, it will be values {0.0, 1.0, ..., numClasses-1}, However, if the training set is missing a label, then all of the arrays over labels (e.g., from truePositiveRateByLabel) will be of length numClasses-1 instead of the expected numClasses.
    
    Returns:
    
    (undocumented)
  - truePositiveRateByLabel
```
double[] truePositiveRateByLabel()
```
    Returns true positive rate for each label (category).
  - falsePositiveRateByLabel
```
double[] falsePositiveRateByLabel()
```
    Returns false positive rate for each label (category).
  - precisionByLabel
```
double[] precisionByLabel()
```
    Returns precision for each label (category).
  - recallByLabel
```
double[] recallByLabel()
```
    Returns recall for each label (category).
  - fMeasureByLabel
```
double[] fMeasureByLabel(double beta)
```
    Returns f-measure for each label (category).
  - fMeasureByLabel
```
double[] fMeasureByLabel()
```
    Returns f1-measure for each label (category).
  - accuracy
```
double accuracy()
```
    Returns accuracy. (equals to the total number of correctly classified instances out of the total number of instances.)
    
    Returns:
    
    (undocumented)
  - weightedTruePositiveRate
```
double weightedTruePositiveRate()
```
    Returns weighted true positive rate. (equals to precision, recall and f-measure)
    
    Returns:
    
    (undocumented)
  - weightedFalsePositiveRate
```
double weightedFalsePositiveRate()
```
    Returns weighted false positive rate.
  - weightedRecall
```
double weightedRecall()
```
    Returns weighted averaged recall. (equals to precision, recall and f-measure)
    
    Returns:
    
    (undocumented)
  - weightedPrecision
```
double weightedPrecision()
```
    Returns weighted averaged precision.
  - weightedFMeasure
```
double weightedFMeasure(double beta)
```
    Returns weighted averaged f-measure.
  - weightedFMeasure
```
double weightedFMeasure()
```
    Returns weighted averaged f1-measure.
  - asBinary
```
BinaryLogisticRegressionSummary asBinary()
```
    Convenient method for casting to binary logistic regression summary. This method will throws an Exception if the summary is not a binary summary.
    
    Returns:
    
    (undocumented)

Interface LogisticRegressionSummary

Method Summary

Method Detail

predictions

probabilityCol

predictionCol

labelCol

featuresCol

multiclassMetrics

labels

truePositiveRateByLabel

falsePositiveRateByLabel

precisionByLabel

recallByLabel

fMeasureByLabel

fMeasureByLabel

accuracy

weightedTruePositiveRate

weightedFalsePositiveRate

weightedRecall

weightedPrecision

weightedFMeasure

weightedFMeasure

asBinary