Round UUID: 9b2dfe62-067a-4419-8e79-fd248cc3bd4b
Prompt:
To classify an entity as Fylaran or Qtharri, use the following refined rules based on the mean attributes, error metrics, and specific thresholds to address edge cases: 1. If 'mean_orbital_radius' is greater than or equal to 3.0 and 'mean_surface_roughness' is greater than or equal to 300, classify as Fylaran unless 'mean_core_density' exceeds 0.91. 2. If 'mean_orbital_radius' is less than 2.6, regardless of other features, classify as Qtharri. 3. If 'mean_core_density' is greater than 0.91, classify as Qtharri. 4. If 'mean_core_density' is less than or equal to 0.85 and 'mean_impact_crater_count' is less than 0.95, classify as Fylaran. 5. If 'mean_orbital_radius' is between 2.6 and 3.0 inclusive, and 'mean_surface_roughness' exceeds 500, classify as Fylaran unless 'mean_core_density' exceeds 0.90 or 'mean_impact_crater_count' exceeds 0.96. 6. If 'mean_surface_roughness' is less than 200 and 'mean_core_density' is between 0.85 and 0.91, classify as Qtharri. For edge cases and new refinements to address misclassifications: 7. If 'mean_core_density' is between 0.89 and 0.92, then: a. If 'mean_surface_roughness' is greater than or equal to 350 and 'core_density_error' is less than 0.1, classify as Fylaran. b. If 'mean_surface_roughness' is less than 350 or 'core_density_error' exceeds 0.15, classify as Qtharri. 8. Enhance the weighted scoring system for borderline cases where none of the above rules apply: - Calculate the weighted score as follows: (**6×mean_orbital_radius** + **3×mean_surface_roughness** - **4×mean_core_density**) divided by the sum of feature coefficients. - If the score exceeds 2.6, classify as Fylaran. - Otherwise, classify as Qtharri. 9. Additionally, prioritize: - If 'core_density_error' exceeds 0.12 and 'mean_axial_symmetry' is below 110, classify as Qtharri. - If 'core_density_error' is less than 0.1 but 'orbital_radius_error' exceeds 0.7, factor in closer proximity to 'mean_orbital_radius' or 'mean_core_density' to determine classification tendency. This revised prompt adds more granular thresholds for borderline mean_core_density values and adjusts edge case handling to enhance recall and reduce both false positives and false negatives.