/*
* RapidMiner
*
* Copyright (C) 2001-2008 by Rapid-I and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapid-i.com
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Affero General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Affero General Public License for more details.
*
* You should have received a copy of the GNU Affero General Public License
* along with this program. If not, see http://www.gnu.org/licenses/.
*/
package com.rapidminer.operator.performance;
/**
* The root-mean-squared error. Mean-squared error is the most commonly used
* measure of success of numeric prediction, and root mean-squared error is the
* square root of mean-squared-error, take to give it the same dimensions as the
* predicted values themselves. This method exaggerates the prediction error -
* the difference between prediction value and actual value of a test case - of
* test cases in which the prediction error is larger than the others. If this
* number is significantly greater than the mean absolute error, it means that
* there are test cases in which the prediction error is significantly greater
* than the average prediction error.
*
* @author Ingo Mierswa, Simon Fischer
* @version $Id: RootMeanSquaredError.java,v 2.3 2006/03/21 15:35:51 ingomierswa
* Exp $
*/
public class RootMeanSquaredError extends SimpleCriterion {
private static final long serialVersionUID = -4425511584684855855L;
public RootMeanSquaredError() {
}
public RootMeanSquaredError(RootMeanSquaredError sc) {
super(sc);
}
public String getName() {
return "root_mean_squared_error";
}
/** Calculates the error for the current example. */
public double countExample(double label, double predictedLabel) {
double dif = label - predictedLabel;
return dif * dif;
}
/** Applies a square root to the given value. */
public double transform(double value) {
return Math.sqrt(value);
}
public String getDescription() {
return "Averaged root-mean-squared error";
}
}