org.apache.spark.mllib.evaluation

Class RegressionMetrics

java.lang.Object

org.apache.spark.mllib.evaluation.RegressionMetrics

All Implemented Interfaces:

Logging

public class RegressionMetrics
extends java.lang.Object
implements Logging

Evaluator for regression.

param: predictionAndObservations an RDD of (prediction, observation) pairs.

Constructor Summary

Constructors

Constructor and Description
RegressionMetrics(RDD<scala.Tuple2<java.lang.Object,java.lang.Object>> predictionAndObservations)

Method Summary

Methods

Modifier and Type	Method and Description
double	explainedVariance() Returns the variance explained by regression.
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.
double	r2() Returns R^2^, the unadjusted coefficient of determination.
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

Methods inherited from interface org.apache.spark.Logging

initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Constructor Detail

RegressionMetrics

public RegressionMetrics(RDD<scala.Tuple2<java.lang.Object,java.lang.Object>> predictionAndObservations)

Method Detail

explainedVariance

public double explainedVariance()

Returns the variance explained by regression. explainedVariance = \sum_i (\hat{y_i} - \bar{y})^2 / n

Returns:

(undocumented)

See Also:

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 unadjusted coefficient of determination.

Returns:

(undocumented)

See Also: