public class LinearRegressionSummary
extends Object
implements scala.Serializable
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.
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()
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 l1norm 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()
Deprecated.
The model field is deprecated and will be removed in 2.1.0. Since 2.0.0.

long 
numInstances()
Number of instances in DataFrame predictions

String 
predictionCol() 
Dataset<Row> 
predictions() 
double[] 
pValues()
Twosided pvalue of estimated coefficients and intercept.

double 
r2()
Returns R^2^, the coefficient of determination.

Dataset<Row> 
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()
Tstatistic of estimated coefficients and intercept.

public String predictionCol()
public String labelCol()
public String featuresCol()
public LinearRegressionModel model()
public double explainedVariance()
http://en.wikipedia.org/wiki/Explained_variation
Note: This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol
.
This will change in later Spark versions.
public double meanAbsoluteError()
Note: This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol
.
This will change in later Spark versions.
public double meanSquaredError()
Note: This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol
.
This will change in later Spark versions.
public double rootMeanSquaredError()
Note: This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol
.
This will change in later Spark versions.
public double r2()
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.
public long numInstances()
public double[] devianceResiduals()
public double[] coefficientStandardErrors()
If LinearRegression.fitIntercept
is set to true,
then the last element returned corresponds to the intercept.
LinearRegression.solver
public double[] tValues()
If LinearRegression.fitIntercept
is set to true,
then the last element returned corresponds to the intercept.
LinearRegression.solver
public double[] pValues()
If LinearRegression.fitIntercept
is set to true,
then the last element returned corresponds to the intercept.
LinearRegression.solver