MLEnsembleManager

Manages ensemble strategies for spatial sampling optimization, combining results from different ML models (BDL, RF, UDL, UFN).

Details

The MLEnsembleManager class provides:

• Unified interface for running multiple ML models

• Ensemble methods: Stacking, Blending, Voting

• Comparative analysis of model performance

Methods

Public methods

• MLEnsembleManager$new()

• MLEnsembleManager$register_model()

• MLEnsembleManager$run_ensemble()

• MLEnsembleManager$compare_models()

• MLEnsembleManager$clone()

---

Method new()

Usage

MLEnsembleManager$new(config = list())

Arguments

config

Optional configuration list Register an ML Model

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Method register_model()

Usage

MLEnsembleManager$register_model(name, model_instance)

Arguments

name

Name of the model (e.g., "BDL", "RF")

model_instance

Instance of the ML model class Run Ensemble Optimization

---

Method run_ensemble()

Usage

MLEnsembleManager$run_ensemble(
  field_data,
  existing_samples,
  n_new_samples,
  method = "voting"
)

Arguments

field_data

Field data

existing_samples

Existing samples

n_new_samples

Number of new samples

method

Ensemble method ("voting", "stacking")

Returns

Ensemble result Compare Registered Models

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Method compare_models()

Usage

MLEnsembleManager$compare_models(field_data, existing_samples, target_variable)

Arguments

field_data

Field data

existing_samples

Existing samples

target_variable

Target variable name

Returns

Comparison metrics

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Method clone()

The objects of this class are cloneable with this method.

Usage

MLEnsembleManager$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.