public Class RegressionMetrics

Class Inheritance

java.lang.Object
javax.visrec.ml.eval.EvaluationMetrics
javax.visrec.ml.eval.RegressionMetrics

Commonly used metrics to estimate how good machine learning model solves some regression task. This class is typed wrapper for EvaluationMetrics which uses map to store metrics, and allows storing and accessing of custom metrics.

Field Summary

Inherited from javax.visrec.ml.eval.EvaluationMetrics:: ACCURACY F1SCORE F_STAT MEAN_ABSOLUTE_ERROR MEAN_SQUARED_ERROR PRECISION R_SQUARED RECALL RESIDUAL_SQUARE_SUM RESIDUAL_STANDARD_ERROR ROOT_MEAN_SQUARED_ERROR

Constructor Summary

Access	Constructor and Description
public	RegressionMetrics()

Method Summary

Modifier and Type	Method and Description
public double	getFstat()
public double	getMeanSquaredError()
public double	getResidualSquareSum()
public double	getResidualStandardError()
public double	getRSquared() Portion of variation explained.

Inherited from javax.visrec.ml.eval.EvaluationMetrics:: get set toString

Constructor Detail

RegressionMetrics	back to summary
public RegressionMetrics()

Method Detail

getFstat	back to summary
public double getFstat()

getMeanSquaredError	back to summary
public double getMeanSquaredError()

getResidualSquareSum	back to summary
public double getResidualSquareSum()

getResidualStandardError	back to summary
public double getResidualStandardError()

getRSquared	back to summary
public double getRSquared() Portion of variation explained. How much better is this than the average.