org.deeplearning4j.eval

Class RegressionEvaluation

java.lang.Object

org.deeplearning4j.eval.BaseEvaluation<RegressionEvaluation>

org.deeplearning4j.eval.RegressionEvaluation

All Implemented Interfaces:

Serializable, IEvaluation<RegressionEvaluation>

public class RegressionEvaluation
extends BaseEvaluation<RegressionEvaluation>

Evaluation method for the evaluation of regression algorithms.
Provides the following metrics, for each column:
- MSE: mean squared error
- MAE: mean absolute error
- RMSE: root mean squared error
- RSE: relative squared error
- correlation coefficient
See for example: http://www.saedsayad.com/model_evaluation_r.htm For classification, see Evaluation

Author:

Alex Black

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static int	DEFAULT_PRECISION

Constructor Summary

Constructors

Constructor and Description
RegressionEvaluation()
RegressionEvaluation(int nColumns) Create a regression evaluation object with the specified number of columns, and default precision for the stats() method.
RegressionEvaluation(int nColumns, int precision) Create a regression evaluation object with the specified number of columns, and specified precision for the stats() method.
RegressionEvaluation(List<String> columnNames) Create a regression evaluation object with default precision for the stats() method
RegressionEvaluation(List<String> columnNames, int precision) Create a regression evaluation object with specified precision for the stats() method
RegressionEvaluation(String... columnNames) Create a regression evaluation object with default precision for the stats() method

Method Summary

Modifier and Type	Method and Description
double	averagecorrelationR2() Average R2 across all columns
double	averageMeanAbsoluteError() Average MAE across all columns
double	averageMeanSquaredError() Average MSE across all columns
double	averagerelativeSquaredError() Average RSE across all columns
double	averagerootMeanSquaredError() Average RMSE across all columns
double	correlationR2(int column)
void	eval(org.nd4j.linalg.api.ndarray.INDArray labels, org.nd4j.linalg.api.ndarray.INDArray predictions)
void	eval(org.nd4j.linalg.api.ndarray.INDArray labels, org.nd4j.linalg.api.ndarray.INDArray predictions, org.nd4j.linalg.api.ndarray.INDArray maskArray)
double	meanAbsoluteError(int column)
double	meanSquaredError(int column)
void	merge(RegressionEvaluation other)
int	numColumns()
double	relativeSquaredError(int column)
void	reset()
double	rootMeanSquaredError(int column)
String	stats()

Methods inherited from class org.deeplearning4j.eval.BaseEvaluation

equals, eval, evalTimeSeries, evalTimeSeries, fromJson, fromYaml, toJson, toString, toYaml

Methods inherited from class java.lang.Object

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

Field Detail

DEFAULT_PRECISION

public static final int DEFAULT_PRECISION

See Also:

Constant Field Values

Constructor Detail

RegressionEvaluation

public RegressionEvaluation()

RegressionEvaluation

public RegressionEvaluation(int nColumns)

Create a regression evaluation object with the specified number of columns, and default precision for the stats() method.

Parameters:

nColumns - Number of columns

RegressionEvaluation

public RegressionEvaluation(int nColumns,
                            int precision)

Create a regression evaluation object with the specified number of columns, and specified precision for the stats() method.

Parameters:

nColumns - Number of columns

RegressionEvaluation

public RegressionEvaluation(String... columnNames)

Create a regression evaluation object with default precision for the stats() method

Parameters:

columnNames - Names of the columns

RegressionEvaluation

public RegressionEvaluation(List<String> columnNames)

Create a regression evaluation object with default precision for the stats() method

Parameters:

columnNames - Names of the columns

RegressionEvaluation

public RegressionEvaluation(List<String> columnNames,
                            int precision)

Create a regression evaluation object with specified precision for the stats() method

Parameters:

columnNames - Names of the columns

Method Detail

reset

public void reset()

eval

public void eval(org.nd4j.linalg.api.ndarray.INDArray labels,
                 org.nd4j.linalg.api.ndarray.INDArray predictions)

eval

public void eval(org.nd4j.linalg.api.ndarray.INDArray labels,
                 org.nd4j.linalg.api.ndarray.INDArray predictions,
                 org.nd4j.linalg.api.ndarray.INDArray maskArray)

Specified by:

eval in interface IEvaluation<RegressionEvaluation>

Overrides:

eval in class BaseEvaluation<RegressionEvaluation>

merge

public void merge(RegressionEvaluation other)

stats

public String stats()

Returns:

numColumns

public int numColumns()

meanSquaredError

public double meanSquaredError(int column)

meanAbsoluteError

public double meanAbsoluteError(int column)

rootMeanSquaredError

public double rootMeanSquaredError(int column)

correlationR2

public double correlationR2(int column)

relativeSquaredError

public double relativeSquaredError(int column)

averageMeanSquaredError

public double averageMeanSquaredError()

Average MSE across all columns

Returns:

averageMeanAbsoluteError

public double averageMeanAbsoluteError()

Average MAE across all columns

Returns:

averagerootMeanSquaredError

public double averagerootMeanSquaredError()

Average RMSE across all columns

Returns:

averagerelativeSquaredError

public double averagerelativeSquaredError()

Average RSE across all columns

Returns:

averagecorrelationR2

public double averagecorrelationR2()

Average R2 across all columns

Returns: