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wiki:Symbolic Regression Problem

Version 5 (modified by jkarder, 10 years ago) (diff)

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Symbolic Regression Problem

Problem Parameters:

Parameter Description
BestKnownQuality The minimal error value that reached by symbolic regression solutions for the problem.
DataAnalysisProblemData The data set, target variable and input variables of the data analysis problem.
Evaluator SymbolicRegressionScaledMeanSquaredErrorEvaluator: The operator which should be used to evaluate symbolic regression solutions.
FunctionTreeGrammar The grammar that should be used for symbolic regression models.
LowerEstimationLimit The lower limit for the estimated value that can be returned by the symbolic regression model.
MaxExpressionDepth Maximal depth of the symbolic expression.
MaxExpressionLength Maximal length of the symbolic expression.
MaxFunctionArguments Maximal number of arguments of automatically defined functions.
MaxFunctionDefiningBranches Maximal number of automatically defined functions.
Maximization Set to false as the error of the regression model should be minimized.
SolutionCreator ProbabilisticTreeCreator: The operator which should be used to create new symbolic regression solutions.
SymbolicExpressionTreeInterpreter The interpreter that should be used to evaluate the symbolic expression tree.
UpperEstimationLimit The upper limit for the estimated value that can be returned by the symbolic regression model.

Is there a sample/tutorial?

Sure. We have configured a standard genetic programming algorithm to solve a symbolic regression problem (Boston Housing dataset):