= 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     || [[Documentation/Reference/Probabilistic Tree Creator|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): 
 * [[UsersSamples#GPSR| Symbolic Regression + Genetic Programming]]