Provides standardized structure for ML-enhanced optimization algorithm outputs following constitutional principles. Includes comprehensive result validation, metadata management, performance tracking, and ML-specific components for unified result handling across BDL, RF, UDL, and UFN methods.
Usage
create_ml_optimization_result(
selected_locations,
existing_samples = NULL,
field_data = NULL,
metrics = NULL,
parameters = NULL,
algorithm_specific = NULL,
metadata = NULL,
ml_components = NULL,
method = "unknown"
)Arguments
- selected_locations
Data frame with new sampling locations
- existing_samples
Data frame with existing sampling locations
- field_data
Reference to input field data
- metrics
Performance metrics list
- parameters
Optimization parameters used
- algorithm_specific
List with algorithm-specific outputs
- metadata
Additional metadata
- ml_components
List with ML-specific components (predictions, uncertainties, etc.)
- method
Character string identifying ML method ("BDL", "RF", "UDL", "UFN", "Ensemble")
Details
MLResults ensures consistent output structure across all ML optimization algorithms with constitutional compliance for performance excellence and spatial analysis standards. Supports multiple ML method outputs and comparison data. Create standardized ML optimization result structure
Examples
if (FALSE) { # \dontrun{
# Create ML optimization result
result <- create_ml_optimization_result(
selected_locations = new_locations_df,
existing_samples = existing_df,
field_data = field_data,
metrics = performance_metrics,
parameters = optimization_params,
ml_components = list(
predictions = prediction_raster,
uncertainties = uncertainty_raster,
feature_importance = importance_scores
),
method = "BDL"
)
} # }