training a model (ff_train)

The ff_train function is designed to train an XGBoost model for deforestation prediction using the ForestForesight algorithm. It takes prepared data (usually from ff_prep), sets up the XGBoost parameters, trains the model, and optionally saves it. The function allows for customization of various XGBoost parameters and supports both training and validation data.

Exercises:

Basic Usage:
Train a simple model using prepared data from ff_prep.

library(ForestForesight)

# Assume we have already prepared data using ff_prep
prepared_data <- ff_prep(...)

model <- ff_train(
  train_matrix = prepared_data$data_matrix,
  nrounds = 100,
  verbose = TRUE
)

print(model)

Using Validation Data:
Train a model with separate validation data for early stopping.

prepared_data <- ff_prep(..., validation_sample = 0.2)

model <- ff_train(
  train_matrix = prepared_data$data_matrix,
  validation_matrix = prepared_data$validation_matrix,
  nrounds = 300,
  early_stopping_rounds = 20,
  verbose = TRUE
)

print(model$best_iteration)
print(model$best_score)

Customizing XGBoost Parameters:
Experiment with different XGBoost parameters to optimize the model.

model <- ff_train(
  train_matrix = prepared_data$data_matrix,
  eta = 0.05,
  max_depth = 7,
  subsample = 0.8,
  min_child_weight = 2,
  eval_metric = "auc",
  verbose = TRUE
)

print(model$params)

Saving the Model:
Train a model and save it to a file for later use.

model <- ff_train(
  train_matrix = prepared_data$data_matrix,
  modelfilename = "my_deforestation_model.model",
  verbose = TRUE
)

# Verify that the model file was created
file.exists("my_deforestation_model.model")

Continue Training from a Saved Model:
Load a previously saved model and continue training it with new data.

# Assume we have a previously saved model
existing_model <- xgboost::xgb.load("my_deforestation_model.model")

# Prepare new data
new_prepared_data <- ff_prep(...)

updated_model <- ff_train(
  train_matrix = new_prepared_data$data_matrix,
  xgb_model = existing_model,
  nrounds = 50,  # Additional rounds to train
  verbose = TRUE
)

print(updated_model$niter)

These exercises will help you understand how to use the ff_train function to train XGBoost models for deforestation prediction, experiment with different parameters, and manage model saving and updating. Remember to replace the ff_prep(...) calls with actual data preparation steps using your ForestForesight data.

training a model (ff_train)

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