LinearRegressionSummary

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

All Implemented Interfaces:

java.io.Serializable

Direct Known Subclasses:

LinearRegressionTrainingSummary
```
public class LinearRegressionSummary
extends java.lang.Object
implements scala.Serializable
```
See Also:
Serialized Form

Method Summary

Methods
Modifier and Type	Method and Description
`double[]`	`coefficientStandardErrors()` Standard error of estimated coefficients and intercept.
`double[]`	`devianceResiduals()` The weighted residuals, the usual residuals rescaled by the square root of the instance weights.
`double`	`explainedVariance()`
`java.lang.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.
`LinearRegressionModel`	`model()`
`long`	`numInstances()` Number of instances in DataFrame predictions
`java.lang.String`	`predictionCol()`
`DataFrame`	`predictions()`
`double[]`	`pValues()` Two-sided p-value of estimated coefficients and intercept.
`double`	`r2()` Returns R^2^, the coefficient of determination.
`DataFrame`	`residuals()` Residuals (label - predicted value)
`double`	`rootMeanSquaredError()` Returns the root mean squared error, which is defined as the square root of the mean squared error.
`double[]`	`tValues()` T-statistic of estimated coefficients and intercept.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Method Detail
  - predictions
```
public DataFrame predictions()
```
  - predictionCol
```
public java.lang.String predictionCol()
```
  - labelCol
```
public java.lang.String labelCol()
```
  - model
```
public LinearRegressionModel model()
```
  - explainedVariance
```
public double explainedVariance()
```
  - 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.
    Note: This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
    
    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.
    Note: This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
    
    Returns:
    (undocumented)
  - rootMeanSquaredError
```
public double rootMeanSquaredError()
```
    Returns the root mean squared error, which is defined as the square root of the mean squared error.
    Note: This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
    
    Returns:
    (undocumented)
  - r2
```
public double r2()
```
    Returns R^2^, the coefficient of determination. Reference: http://en.wikipedia.org/wiki/Coefficient_of_determination
    Note: This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
    
    Returns:
    (undocumented)
  - residuals
```
public DataFrame residuals()
```
    Residuals (label - predicted value)
  - numInstances
```
public long numInstances()
```
    Number of instances in DataFrame predictions
  - devianceResiduals
```
public double[] devianceResiduals()
```
    The weighted residuals, the usual residuals rescaled by the square root of the instance weights.
    
    Returns:
    (undocumented)
  - coefficientStandardErrors
```
public double[] coefficientStandardErrors()
```
    Standard error of estimated coefficients and intercept.
    
    Returns:
    (undocumented)
  - tValues
```
public double[] tValues()
```
    T-statistic of estimated coefficients and intercept.
    
    Returns:
    (undocumented)
  - pValues
```
public double[] pValues()
```
    Two-sided p-value of estimated coefficients and intercept.
    
    Returns:
    (undocumented)

Class LinearRegressionSummary

Method Summary

Methods inherited from class java.lang.Object

Method Detail

predictions

predictionCol

labelCol

model

explainedVariance

meanAbsoluteError

meanSquaredError

rootMeanSquaredError

r2

residuals

numInstances

devianceResiduals

coefficientStandardErrors

tValues

pValues