LinearRegressionTrainingSummary

class LinearRegressionTrainingSummary extends LinearRegressionSummary

Linear regression training results. Currently, the training summary ignores the training weights except for the objective trace.

Annotations: @Since( "1.5.0" )
Source: LinearRegression.scala

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LinearRegressionSummary, Serializable, Serializable, AnyRef, Any

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LinearRegressionTrainingSummary
LinearRegressionSummary
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final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: Any): Boolean

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final def asInstanceOf[T0]: T0

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def clone(): AnyRef

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protected[lang]
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@throws( ... ) @native()
lazy val coefficientStandardErrors: Array[Double]
Standard error of estimated coefficients and intercept.
Standard error of estimated coefficients and intercept. This value is only available when using the "normal" solver.
If LinearRegression.fitIntercept is set to true, then the last element returned corresponds to the intercept.

Definition Classes
LinearRegressionSummary
See also
LinearRegression.solver
val degreesOfFreedom: Long
Degrees of freedom
Degrees of freedom

Definition Classes
LinearRegressionSummary
Annotations
@Since( "2.2.0" )
lazy val devianceResiduals: Array[Double]
The weighted residuals, the usual residuals rescaled by the square root of the instance weights.
The weighted residuals, the usual residuals rescaled by the square root of the instance weights.

Definition Classes
LinearRegressionSummary
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
val explainedVariance: Double
Returns the explained variance regression score.
Returns the explained variance regression score. explainedVariance = 1 - variance(y - \hat{y}) / variance(y) Reference: Wikipedia explain variation

Definition Classes
LinearRegressionSummary
Annotations
@Since( "1.5.0" )
val featuresCol: String

Definition Classes
LinearRegressionSummary
def finalize(): Unit

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protected[lang]
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@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

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@native()
def hashCode(): Int

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@native()
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
val labelCol: String

Definition Classes
LinearRegressionSummary
val meanAbsoluteError: Double
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 the mean absolute error, which is a risk function corresponding to the expected value of the absolute error loss or l1-norm loss.

Definition Classes
LinearRegressionSummary
Annotations
@Since( "1.5.0" )
val meanSquaredError: Double
Returns the mean squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss.
Returns the mean squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss.

Definition Classes
LinearRegressionSummary
Annotations
@Since( "1.5.0" )
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
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@native()
final def notifyAll(): Unit

Definition Classes
AnyRef
Annotations
@native()
lazy val numInstances: Long
Number of instances in DataFrame predictions
Number of instances in DataFrame predictions

Definition Classes
LinearRegressionSummary
val objectiveHistory: Array[Double]
lazy val pValues: Array[Double]
Two-sided p-value of estimated coefficients and intercept.
Two-sided p-value of estimated coefficients and intercept. This value is only available when using the "normal" solver.
If LinearRegression.fitIntercept is set to true, then the last element returned corresponds to the intercept.

Definition Classes
LinearRegressionSummary
See also
LinearRegression.solver
val predictionCol: String

Definition Classes
LinearRegressionSummary
val predictions: DataFrame

Definition Classes
LinearRegressionSummary
val r2: Double
Returns R², the coefficient of determination.
Returns R², the coefficient of determination. Reference: Wikipedia coefficient of determination

Definition Classes
LinearRegressionSummary
Annotations
@Since( "1.5.0" )
val r2adj: Double
Returns Adjusted R², the adjusted coefficient of determination.
Returns Adjusted R², the adjusted coefficient of determination. Reference: Wikipedia coefficient of determination

Definition Classes
LinearRegressionSummary
Annotations
@Since( "2.3.0" )
lazy val residuals: DataFrame
Residuals (label - predicted value)
Residuals (label - predicted value)

Definition Classes
LinearRegressionSummary
Annotations
@Since( "1.5.0" ) @transient()
val rootMeanSquaredError: Double
Returns the root mean squared error, which is defined as the square root of the mean squared error.
Returns the root mean squared error, which is defined as the square root of the mean squared error.

Definition Classes
LinearRegressionSummary
Annotations
@Since( "1.5.0" )
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
lazy val tValues: Array[Double]
T-statistic of estimated coefficients and intercept.
T-statistic of estimated coefficients and intercept. This value is only available when using the "normal" solver.
If LinearRegression.fitIntercept is set to true, then the last element returned corresponds to the intercept.

Definition Classes
LinearRegressionSummary
See also
LinearRegression.solver
def toString(): String

Definition Classes
AnyRef → Any
val totalIterations: Int
Number of training iterations until termination
Number of training iterations until termination
This value is only available when using the "l-bfgs" solver.

Annotations
@Since( "1.5.0" )
See also
LinearRegression.solver
final def wait(): Unit

Definition Classes
AnyRef
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@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... ) @native()

Inherited from LinearRegressionSummary

Value Members

lazy val coefficientStandardErrors: Array[Double]
Standard error of estimated coefficients and intercept.
Standard error of estimated coefficients and intercept. This value is only available when using the "normal" solver.
If LinearRegression.fitIntercept is set to true, then the last element returned corresponds to the intercept.

Definition Classes
LinearRegressionSummary
See also
LinearRegression.solver
val degreesOfFreedom: Long
Degrees of freedom
Degrees of freedom

Definition Classes
LinearRegressionSummary
Annotations
@Since( "2.2.0" )
lazy val devianceResiduals: Array[Double]
The weighted residuals, the usual residuals rescaled by the square root of the instance weights.
The weighted residuals, the usual residuals rescaled by the square root of the instance weights.

Definition Classes
LinearRegressionSummary
val explainedVariance: Double
Returns the explained variance regression score.
Returns the explained variance regression score. explainedVariance = 1 - variance(y - \hat{y}) / variance(y) Reference: Wikipedia explain variation

Definition Classes
LinearRegressionSummary
Annotations
@Since( "1.5.0" )
val featuresCol: String

Definition Classes
LinearRegressionSummary
val labelCol: String

Definition Classes
LinearRegressionSummary
val meanAbsoluteError: Double
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 the mean absolute error, which is a risk function corresponding to the expected value of the absolute error loss or l1-norm loss.

Definition Classes
LinearRegressionSummary
Annotations
@Since( "1.5.0" )
val meanSquaredError: Double
Returns the mean squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss.
Returns the mean squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss.

Definition Classes
LinearRegressionSummary
Annotations
@Since( "1.5.0" )
lazy val numInstances: Long
Number of instances in DataFrame predictions
Number of instances in DataFrame predictions

Definition Classes
LinearRegressionSummary
lazy val pValues: Array[Double]
Two-sided p-value of estimated coefficients and intercept.
Two-sided p-value of estimated coefficients and intercept. This value is only available when using the "normal" solver.
If LinearRegression.fitIntercept is set to true, then the last element returned corresponds to the intercept.

Definition Classes
LinearRegressionSummary
See also
LinearRegression.solver
val predictionCol: String

Definition Classes
LinearRegressionSummary
val predictions: DataFrame

Definition Classes
LinearRegressionSummary
val r2: Double
Returns R², the coefficient of determination.
Returns R², the coefficient of determination. Reference: Wikipedia coefficient of determination

Definition Classes
LinearRegressionSummary
Annotations
@Since( "1.5.0" )
val r2adj: Double
Returns Adjusted R², the adjusted coefficient of determination.
Returns Adjusted R², the adjusted coefficient of determination. Reference: Wikipedia coefficient of determination

Definition Classes
LinearRegressionSummary
Annotations
@Since( "2.3.0" )
lazy val residuals: DataFrame
Residuals (label - predicted value)
Residuals (label - predicted value)

Definition Classes
LinearRegressionSummary
Annotations
@Since( "1.5.0" ) @transient()
val rootMeanSquaredError: Double
Returns the root mean squared error, which is defined as the square root of the mean squared error.
Returns the root mean squared error, which is defined as the square root of the mean squared error.

Definition Classes
LinearRegressionSummary
Annotations
@Since( "1.5.0" )
lazy val tValues: Array[Double]
T-statistic of estimated coefficients and intercept.
T-statistic of estimated coefficients and intercept. This value is only available when using the "normal" solver.
If LinearRegression.fitIntercept is set to true, then the last element returned corresponds to the intercept.

Definition Classes
LinearRegressionSummary
See also
LinearRegression.solver

Inherited from AnyRef

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def clone(): AnyRef

Attributes
protected[lang]
Definition Classes
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@throws( ... ) @native()
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
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def finalize(): Unit

Attributes
protected[lang]
Definition Classes
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@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

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Annotations
@native()
def hashCode(): Int

Definition Classes
AnyRef → Any
Annotations
@native()
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
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@native()
final def notifyAll(): Unit

Definition Classes
AnyRef
Annotations
@native()
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... ) @native()

Inherited from Any

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final def asInstanceOf[T0]: T0

Definition Classes
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final def isInstanceOf[T0]: Boolean

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Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( ... ) @native()
lazy val coefficientStandardErrors: Array[Double]
Standard error of estimated coefficients and intercept.
Standard error of estimated coefficients and intercept. This value is only available when using the "normal" solver.
If LinearRegression.fitIntercept is set to true, then the last element returned corresponds to the intercept.

Definition Classes
LinearRegressionSummary
See also
LinearRegression.solver
val degreesOfFreedom: Long
Degrees of freedom
Degrees of freedom

Definition Classes
LinearRegressionSummary
Annotations
@Since( "2.2.0" )
lazy val devianceResiduals: Array[Double]
The weighted residuals, the usual residuals rescaled by the square root of the instance weights.
The weighted residuals, the usual residuals rescaled by the square root of the instance weights.

Definition Classes
LinearRegressionSummary
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
val explainedVariance: Double
Returns the explained variance regression score.
Returns the explained variance regression score. explainedVariance = 1 - variance(y - \hat{y}) / variance(y) Reference: Wikipedia explain variation

Definition Classes
LinearRegressionSummary
Annotations
@Since( "1.5.0" )
val featuresCol: String

Definition Classes
LinearRegressionSummary
def finalize(): Unit

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
Annotations
@native()
def hashCode(): Int

Definition Classes
AnyRef → Any
Annotations
@native()
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
val labelCol: String

Definition Classes
LinearRegressionSummary
val meanAbsoluteError: Double
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 the mean absolute error, which is a risk function corresponding to the expected value of the absolute error loss or l1-norm loss.

Definition Classes
LinearRegressionSummary
Annotations
@Since( "1.5.0" )
val meanSquaredError: Double
Returns the mean squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss.
Returns the mean squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss.

Definition Classes
LinearRegressionSummary
Annotations
@Since( "1.5.0" )
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
Annotations
@native()
final def notifyAll(): Unit

Definition Classes
AnyRef
Annotations
@native()
lazy val numInstances: Long
Number of instances in DataFrame predictions
Number of instances in DataFrame predictions

Definition Classes
LinearRegressionSummary
val objectiveHistory: Array[Double]
lazy val pValues: Array[Double]
Two-sided p-value of estimated coefficients and intercept.
Two-sided p-value of estimated coefficients and intercept. This value is only available when using the "normal" solver.
If LinearRegression.fitIntercept is set to true, then the last element returned corresponds to the intercept.

Definition Classes
LinearRegressionSummary
See also
LinearRegression.solver
val predictionCol: String

Definition Classes
LinearRegressionSummary
val predictions: DataFrame

Definition Classes
LinearRegressionSummary
val r2: Double
Returns R², the coefficient of determination.
Returns R², the coefficient of determination. Reference: Wikipedia coefficient of determination

Definition Classes
LinearRegressionSummary
Annotations
@Since( "1.5.0" )
val r2adj: Double
Returns Adjusted R², the adjusted coefficient of determination.
Returns Adjusted R², the adjusted coefficient of determination. Reference: Wikipedia coefficient of determination

Definition Classes
LinearRegressionSummary
Annotations
@Since( "2.3.0" )
lazy val residuals: DataFrame
Residuals (label - predicted value)
Residuals (label - predicted value)

Definition Classes
LinearRegressionSummary
Annotations
@Since( "1.5.0" ) @transient()
val rootMeanSquaredError: Double
Returns the root mean squared error, which is defined as the square root of the mean squared error.
Returns the root mean squared error, which is defined as the square root of the mean squared error.

Definition Classes
LinearRegressionSummary
Annotations
@Since( "1.5.0" )
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
lazy val tValues: Array[Double]
T-statistic of estimated coefficients and intercept.
T-statistic of estimated coefficients and intercept. This value is only available when using the "normal" solver.
If LinearRegression.fitIntercept is set to true, then the last element returned corresponds to the intercept.

Definition Classes
LinearRegressionSummary
See also
LinearRegression.solver
def toString(): String

Definition Classes
AnyRef → Any
val totalIterations: Int
Number of training iterations until termination
Number of training iterations until termination
This value is only available when using the "l-bfgs" solver.

Annotations
@Since( "1.5.0" )
See also
LinearRegression.solver
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... ) @native()

Packages

LinearRegressionTrainingSummary 

class LinearRegressionTrainingSummary extends LinearRegressionSummary

Value Members

Inherited from LinearRegressionSummary

Value Members

Inherited from AnyRef

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Inherited from Any

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Members

LinearRegressionTrainingSummary