Pareto Front API¶
ParetoFront(project_id, error_metric, hyperparameters, target_type, solutions)¶
Pareto front data for a Eureqa model.
The pareto front reflects the tradeoffs between error and complexity for particular model. The solutions reflect possible Eureqa models that are different levels of complexity. By default, only one solution will have a corresponding model, but models can be created for each solution.
project_id (str) the ID of the project the model belongs to error_metric (str) Eureqa error-metric identifier used to compute error metrics for this search. Note that Eureqa error metrics do NOT correspond 1:1 with DataRobot error metrics – the available metrics are not the same, and are computed from a subset of the training data rather than from the validation data. hyperparameters (dict) Hyperparameters used by this run of the Eureqa blueprint target_type (str) Indicating what kind of modeling is being done in this project, either ‘Regression’, ‘Binary’ (Binary classification), or ‘Multiclass’ (Multiclass classification). solutions (list(Solution)) Solutions that Eureqa has found to model this data. Some solutions will have greater accuracy. Others will have slightly less accuracy but will use simpler expressions.
Solution(eureqa_solution_id, complexity, error, expression, expression_annotated, best_model, project_id)¶
A solution represents a possible Eureqa model; however not all solutions have models associated with them. It must have a model created before it can be used to make predictions, etc.
eureqa_solution_id: str ID of this Solution complexity: int Complexity score for this solution. Complexity score is a function of the mathematical operators used in the current solution. The Complexity calculation can be tuned via model hyperparameters. error: float Error for the current solution, as computed by Eureqa using the ‘error_metric’ error metric. expression: str Eureqa model equation string. expression_annotated: str Eureqa model equation string with variable names tagged for easy identification. best_model: bool True, if the model is determined to be the best
Add this solution to the leaderboard, if it is not already present.