LinearRegressionSummary (Spark 3.5.4 JavaDoc)

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

All Implemented Interfaces:

java.io.Serializable

Direct Known Subclasses:

LinearRegressionTrainingSummary
```
public class LinearRegressionSummary
extends Object
implements scala.Serializable
```
Linear regression results evaluated on a dataset.
param: predictions predictions output by the model's transform method. param: predictionCol Field in "predictions" which gives the predicted value of the label at each instance. 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

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`double[]`	`coefficientStandardErrors()`
`long`	`degreesOfFreedom()` Degrees of freedom
`double[]`	`devianceResiduals()`
`double`	`explainedVariance()` Returns the explained variance regression score.
`String`	`featuresCol()`
`String`	`labelCol()`
`double`	`meanAbsoluteError()` Returns the mean absolute error, which is a risk function corresponding to the expected value of the absolute error loss or l1-norm loss.
`double`	`meanSquaredError()` Returns the mean squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss.
`long`	`numInstances()`
`String`	`predictionCol()`
`Dataset<Row>`	`predictions()`
`double[]`	`pValues()`
`double`	`r2()` Returns R^2^, the coefficient of determination.
`double`	`r2adj()` Returns Adjusted R^2^, the adjusted coefficient of determination.
`Dataset<Row>`	`residuals()`
`double`	`rootMeanSquaredError()` Returns the root mean squared error, which is defined as the square root of the mean squared error.
`double[]`	`tValues()`

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

- Method Detail
  - coefficientStandardErrors
```
public double[] coefficientStandardErrors()
```
  - degreesOfFreedom
```
public long degreesOfFreedom()
```
    Degrees of freedom
  - devianceResiduals
```
public double[] devianceResiduals()
```
  - explainedVariance
```
public double explainedVariance()
```
    Returns the explained variance regression score. explainedVariance = 1 - variance(y - \hat{y}) / variance(y) Reference: Wikipedia explain variation
    
    Returns:
    
    (undocumented)
  - featuresCol
```
public String featuresCol()
```
  - labelCol
```
public String labelCol()
```
  - meanAbsoluteError
```
public double meanAbsoluteError()
```
    Returns the mean absolute error, which is a risk function corresponding to the expected value of the absolute error loss or l1-norm loss.
    
    Returns:
    
    (undocumented)
  - meanSquaredError
```
public double meanSquaredError()
```
    Returns the mean squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss.
    
    Returns:
    
    (undocumented)
  - numInstances
```
public long numInstances()
```
  - pValues
```
public double[] pValues()
```
  - predictionCol
```
public String predictionCol()
```
  - predictions
```
public Dataset<Row> predictions()
```
  - r2
```
public double r2()
```
    Returns R^2^, the coefficient of determination. Reference: Wikipedia coefficient of determination
    
    Returns:
    
    (undocumented)
  - r2adj
```
public double r2adj()
```
    Returns Adjusted R^2^, the adjusted coefficient of determination. Reference: Wikipedia coefficient of determination
    
    Returns:
    
    (undocumented)
  - residuals
```
public Dataset<Row> residuals()
```
  - rootMeanSquaredError
```
public double rootMeanSquaredError()
```
    Returns the root mean squared error, which is defined as the square root of the mean squared error.
    
    Returns:
    
    (undocumented)
  - tValues
```
public double[] tValues()
```

Class LinearRegressionSummary

Method Summary

Methods inherited from class Object

Method Detail

coefficientStandardErrors

degreesOfFreedom

devianceResiduals

explainedVariance

featuresCol

labelCol

meanAbsoluteError

meanSquaredError

numInstances

pValues

predictionCol

predictions

r2

r2adj

residuals

rootMeanSquaredError

tValues