RandomForestOptimization

Implements Random Forest techniques for spatial sampling optimization, focusing on feature importance analysis and spatial autocorrelation handling.

Details

The RandomForestOptimization class provides:

• Feature importance-based sampling location optimization

• Spatial Random Forest implementation with autocorrelation support

• Hyperparameter tuning for optimal model performance

• Support for both regression and classification tasks

Public fields

model

Trained Random Forest model

config

Configuration parameters for RF optimization

importance_cache

Cached feature importance results

Active bindings

model

Trained Random Forest model

config

Configuration parameters for RF optimization

importance_cache

Cached feature importance results

Methods

Public methods

• RandomForestOptimization$new()

• RandomForestOptimization$fit_model()

• RandomForestOptimization$optimize_locations()

• RandomForestOptimization$get_feature_importance()

• RandomForestOptimization$clone()

---

Method new()

Usage

RandomForestOptimization$new(config = list())

Arguments

config

Optional configuration list for RF parameters

Examples

\dontrun{
rf_opt <- RandomForestOptimization$new()
rf_custom <- RandomForestOptimization$new(config = list(ntree = 1000))
}

---

Method fit_model()

Usage

RandomForestOptimization$fit_model(
  field_data,
  existing_samples,
  target_variable = NULL,
  perform_tuning = FALSE
)

Arguments

field_data

List containing boundary, covariates, and metadata

existing_samples

Data frame or sf object with existing samples

target_variable

Name of target variable to predict

perform_tuning

Whether to perform hyperparameter tuning

Returns

Fitted RF model with performance metrics Optimize sampling locations using Random Forest

---

Method optimize_locations()

Usage

RandomForestOptimization$optimize_locations(
  field_data,
  n_new_samples,
  candidate_points = NULL,
  strategy = "importance"
)

Arguments

field_data

List containing boundary, covariates, and metadata

n_new_samples

Number of new samples to select

candidate_points

Optional set of candidate points

strategy

Sampling strategy ("importance", "uncertainty", "hybrid")

Returns

Data frame of optimized sample locations Get Feature Importance

---

Method get_feature_importance()

Usage

RandomForestOptimization$get_feature_importance()

Returns

Data frame of feature importance scores

---

Method clone()

The objects of this class are cloneable with this method.

Usage

RandomForestOptimization$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (FALSE) { # \dontrun{
# Initialize RF module
rf_opt <- RandomForestOptimization$new()

# Fit model and calculate importance
rf_opt$fit_model(field_data, existing_samples)

# Optimize new locations based on feature importance
new_locations <- rf_opt$optimize_locations(
  field_data = field_data,
  n_new_samples = 20
)
} # }


## ------------------------------------------------
## Method `RandomForestOptimization$new`
## ------------------------------------------------

if (FALSE) { # \dontrun{
rf_opt <- RandomForestOptimization$new()
rf_custom <- RandomForestOptimization$new(config = list(ntree = 1000))
} # }