org.apache.spark.ml.regression

Class 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

:: Experimental :: Linear regression results evaluated on a dataset. param: predictions predictions outputted by the model's transform method.

See Also:

Serialized Form

Method Summary

Methods

Modifier and Type	Method and Description
double	explainedVariance() Returns the explained variance regression score.
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.
java.lang.String	predictionCol()
DataFrame	predictions()
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.

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()

explainedVariance

public double explainedVariance()

Returns the explained variance regression score. explainedVariance = 1 - variance(y - \hat{y}) / variance(y) Reference: http://en.wikipedia.org/wiki/Explained_variation

Returns:

(undocumented)

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)

rootMeanSquaredError

public double rootMeanSquaredError()

Returns the root mean squared error, which is defined as the square root of the mean squared error.

Returns:

(undocumented)

r2

public double r2()

Returns R^2^, the coefficient of determination. Reference: http://en.wikipedia.org/wiki/Coefficient_of_determination

Returns:

(undocumented)

residuals

public DataFrame residuals()

Residuals (label - predicted value)