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The ff_predict function is designed to make predictions using a trained XGBoost model from the ForestForesight algorithm. It can handle both loaded models and model file paths, applies the model to test data, and can optionally create a predicted raster. The function also calculates performance metrics if ground truth data is provided.

Exercises

  1. Basic Usage:
    Make predictions using a trained model and test data.

library(ForestForesight)

# Assume we have a trained model and prepared test data
model <- ... # Trained model from ff_train
test_data <- ff_prep(...) # Prepare test data

predictions <- ff_predict(
  model = model,
  test_matrix = test_data$data_matrix,
  verbose = TRUE
)

print(head(predictions$predictions))

  1. Using a Saved Model File:
    Load a model from a file and make predictions.

predictions <- ff_predict(
  model = "path/to/my_deforestation_model.model",
  test_matrix = test_data$data_matrix,
  verbose = TRUE
)

print(summary(predictions$predictions))

  1. Adjusting the Threshold:
    Experiment with different probability thresholds for binary classification.

predictions <- ff_predict(
  model = model,
  test_matrix = test_data$data_matrix,
  threshold = 0.7,
  verbose = TRUE
)

print(table(predictions$predicted_raster[] > 0.7))

  1. Performance Evaluation:
    Use ground truth data to evaluate model performance.

predictions_with_metrics <- ff_predict(
  model = model,
  test_matrix = test_data$data_matrix,
  groundtruth = test_data$groundtruthraster,
  verbose = TRUE
)

print(predictions_with_metrics$precision)
print(predictions_with_metrics$recall)
print(predictions_with_metrics$F0.5)

  1. Creating a Predicted Raster:
    Generate a spatial raster of predictions.

predictions_with_raster <- ff_predict(
  model = model,
  test_matrix = test_data$data_matrix,
  indices = test_data$testindices,
  templateraster = test_data$groundtruthraster,
  verbose = TRUE
)

# Plot the predicted raster
plot(predictions_with_raster$predicted_raster, main = "Deforestation Prediction")

  1. Generating Probability Maps:
    Create a raster of deforestation probabilities instead of binary classification.

probability_predictions <- ff_predict(
  model = model,
  test_matrix = test_data$data_matrix,
  indices = test_data$testindices,
  templateraster = test_data$groundtruthraster,
  certainty = TRUE,
  verbose = TRUE
)

# Plot the probability map
plot(probability_predictions$predicted_raster, main = "Deforestation Probability")

These exercises will help you understand how to use the ff_predict function to make predictions with trained ForestForesight models, evaluate model performance, and create spatial predictions. Remember to replace the placeholder data (like model and test_data) with actual trained models and prepared data from your ForestForesight workflow.

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