GeneralizedLinearRegressionSummary (Spark 2.3.4 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
  - 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)
  - numInstances
```
public long numInstances()
```
    Number of instances in DataFrame predictions.
  - rank
```
public long rank()
```
    The numeric rank of the fitted linear model.
  - degreesOfFreedom
```
public long degreesOfFreedom()
```
    Degrees of freedom.
  - 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)
  - nullDeviance
```
public double nullDeviance()
```
    The deviance for the null model.
    
    Returns:
    
    (undocumented)
  - 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)
  - aic
```
public double aic()
```
    Akaike Information Criterion (AIC) for the fitted model.

Class GeneralizedLinearRegressionSummary

Method Summary

Methods inherited from class Object

Method Detail

predictionCol

predictions

numInstances

rank

degreesOfFreedom

residualDegreeOfFreedom

residualDegreeOfFreedomNull

residuals

residuals

nullDeviance

deviance

dispersion

aic