javax.visrec.ml.eval

Class EvaluationMetrics

java.lang.Object

javax.visrec.ml.eval.EvaluationMetrics

public class EvaluationMetrics
extends Object

Wrapper for constants and values for classifier and regressor evaluation metrics.

Author:

Zoran Sevarac

Field Summary

Fields

Modifier and Type	Field and Description
static String	ACCURACY
static String	F_STAT Is there a relationship between inputs and predictions(outputs) at all.
static String	F1SCORE
static String	MEAN_ABSOLUTE_ERROR Mean value of sum of squared errors.
static String	MEAN_SQUARED_ERROR
static String	PRECISION
static String	R_SQUARED Percent of variation explained by the regression model.
static String	RECALL
static String	RESIDUAL_SQUARE_SUM
static String	RESIDUAL_STANDARD_ERROR Estimation of standard deviation of prediction errors for some given data set.
static String	ROOT_MEAN_SQUARED_ERROR

Constructor Summary

Constructors

Constructor and Description
EvaluationMetrics()

Method Summary

Modifier and Type	Method and Description
float	get(String key)
void	set(String key, float value)
String	toString()

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

MEAN_ABSOLUTE_ERROR

public static final String MEAN_ABSOLUTE_ERROR

Mean value of sum of squared errors. Errors are squared in order to better explain variability, and take into account positive and negative errors. Regression metrics

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Constant Field Values

MEAN_SQUARED_ERROR

public static final String MEAN_SQUARED_ERROR

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Constant Field Values

ROOT_MEAN_SQUARED_ERROR

public static final String ROOT_MEAN_SQUARED_ERROR

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Constant Field Values

RESIDUAL_SQUARE_SUM

public static final String RESIDUAL_SQUARE_SUM

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Constant Field Values

RESIDUAL_STANDARD_ERROR

public static final String RESIDUAL_STANDARD_ERROR

Estimation of standard deviation of prediction errors for some given data set. Smaller is better.

See Also:

Constant Field Values

R_SQUARED

public static final String R_SQUARED

Percent of variation explained by the regression model. 1 is good , 0 is bad, bigger is better.

See Also:

Constant Field Values

F_STAT

public static final String F_STAT

Is there a relationship between inputs and predictions(outputs) at all. If there is a relationship, this value is greater then 1. When there is now relationship, this value is around 1.

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Constant Field Values

ACCURACY

public static final String ACCURACY

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Constant Field Values

PRECISION

public static final String PRECISION

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Constant Field Values

RECALL

public static final String RECALL

See Also:

Constant Field Values

F1SCORE

public static final String F1SCORE

See Also:

Constant Field Values

Constructor Detail

EvaluationMetrics

public EvaluationMetrics()

Method Detail

get

public float get(String key)

set

public void set(String key,
                float value)

toString

public String toString()

Overrides:

toString in class Object