| | 1 | = Symbolic Regression Problem = |
| | 2 | |
| | 3 | '''Problem Parameters:''' |
| | 4 | ||= Parameter =||= Description =|| |
| | 5 | || !BestKnownQuality || The minimal error value that reached by symbolic regression solutions for the problem. || |
| | 6 | || !DataAnalysisProblemData || The data set, target variable and input variables of the data analysis problem. || |
| | 7 | || Evaluator || !SymbolicRegressionScaledMeanSquaredErrorEvaluator: The operator which should be used to evaluate symbolic regression solutions. || |
| | 8 | || !FunctionTreeGrammar || The grammar that should be used for symbolic regression models. || |
| | 9 | || !LowerEstimationLimit || The lower limit for the estimated value that can be returned by the symbolic regression model. || |
| | 10 | || !MaxExpressionDepth || Maximal depth of the symbolic expression. || |
| | 11 | || !MaxExpressionLength || Maximal length of the symbolic expression. || |
| | 12 | || !MaxFunctionArguments || Maximal number of arguments of automatically defined functions. || |
| | 13 | || !MaxFunctionDefiningBranches || Maximal number of automatically defined functions. || |
| | 14 | || Maximization || Set to false as the error of the regression model should be minimized. || |
| | 15 | || !SolutionCreator || !ProbabilisticTreeCreator: The operator which should be used to create new symbolic regression solutions. || |
| | 16 | || !SymbolicExpressionTreeInterpreter || The interpreter that should be used to evaluate the symbolic expression tree. || |
| | 17 | || !UpperEstimationLimit || The upper limit for the estimated value that can be returned by the symbolic regression model. || |
