GeneralizedLinearRegressionSummary (Spark 2.4.8 JavaDoc)

Object
- org.apache.spark.ml.regression.GeneralizedLinearRegressionSummary

All Implemented Interfaces:

java.io.Serializable

Direct Known Subclasses:

GeneralizedLinearRegressionTrainingSummary
```
public class GeneralizedLinearRegressionSummary
extends Object
implements scala.Serializable
```
:: Experimental :: Summary of GeneralizedLinearRegression model and predictions.
param: dataset Dataset to be summarized. param: origModel Model to be summarized. This is copied to create an internal model which cannot be modified from outside.

See Also:

Serialized Form

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`double`	`aic()` Akaike Information Criterion (AIC) for the fitted model.
`long`	`degreesOfFreedom()` Degrees of freedom.
`double`	`deviance()` The deviance for the fitted model.
`double`	`dispersion()` The dispersion of the fitted model.
`double`	`nullDeviance()` The deviance for the null model.
`long`	`numInstances()` Number of instances in DataFrame predictions.
`String`	`predictionCol()` Field in "predictions" which gives the predicted value of each instance.
`Dataset<Row>`	`predictions()` Predictions output by the model's `transform` method.
`long`	`rank()` The numeric rank of the fitted linear model.
`long`	`residualDegreeOfFreedom()` The residual degrees of freedom.
`long`	`residualDegreeOfFreedomNull()` The residual degrees of freedom for the null model.
`Dataset<Row>`	`residuals()` Get the default residuals (deviance residuals) of the fitted model.
`Dataset<Row>`	`residuals(String residualsType)` Get the residuals of the fitted model by type.

Methods inherited from class Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Method Detail
  - aic
```
public double aic()
```
    Akaike Information Criterion (AIC) for the fitted model.
  - degreesOfFreedom
```
public long degreesOfFreedom()
```
    Degrees of freedom.
  - deviance
```
public double deviance()
```
    The deviance for the fitted model.
    
    Returns:
    
    (undocumented)
  - dispersion
```
public double dispersion()
```
    The dispersion of the fitted model. It is taken as 1.0 for the "binomial" and "poisson" families, and otherwise estimated by the residual Pearson's Chi-Squared statistic (which is defined as sum of the squares of the Pearson residuals) divided by the residual degrees of freedom.
    
    Returns:
    
    (undocumented)
  - nullDeviance
```
public double nullDeviance()
```
    The deviance for the null model.
    
    Returns:
    
    (undocumented)
  - numInstances
```
public long numInstances()
```
    Number of instances in DataFrame predictions.
  - predictionCol
```
public String predictionCol()
```
    Field in "predictions" which gives the predicted value of each instance. This is set to a new column name if the original model's predictionCol is not set.
    
    Returns:
    
    (undocumented)
  - predictions
```
public Dataset<Row> predictions()
```
    Predictions output by the model's transform method.
    
    Returns:
    
    (undocumented)
  - rank
```
public long rank()
```
    The numeric rank of the fitted linear model.
  - residualDegreeOfFreedom
```
public long residualDegreeOfFreedom()
```
    The residual degrees of freedom.
  - residualDegreeOfFreedomNull
```
public long residualDegreeOfFreedomNull()
```
    The residual degrees of freedom for the null model.
  - residuals
```
public Dataset<Row> residuals()
```
    Get the default residuals (deviance residuals) of the fitted model.
    
    Returns:
    
    (undocumented)
  - residuals
```
public Dataset<Row> residuals(String residualsType)
```
    Get the residuals of the fitted model by type.
    
    Parameters:
    
    residualsType - The type of residuals which should be returned. Supported options: deviance, pearson, working and response.
    
    Returns:
    
    (undocumented)

Class GeneralizedLinearRegressionSummary

Method Summary

Methods inherited from class Object

Method Detail

aic

degreesOfFreedom

deviance

dispersion

nullDeviance

numInstances

predictionCol

predictions

rank

residualDegreeOfFreedom

residualDegreeOfFreedomNull

residuals

residuals