Train extreme gradient boosting models — sits

This function uses the extreme gradient boosting algorithm. Boosting iteratively adds basis functions in a greedy fashion so that each new basis function further reduces the selected loss function. This function is a front-end to the methods in the "xgboost" package. Please refer to the documentation in that package for more details.

Usage

sits_xgboost(
  samples = NULL,
  learning_rate = 0.15,
  min_split_loss = 1,
  max_depth = 5L,
  min_child_weight = 1,
  max_delta_step = 1,
  subsample = 0.85,
  nfold = 5L,
  nrounds = 100L,
  nthread = 6L,
  early_stopping_rounds = 20L,
  verbose = FALSE
)

Arguments

samples: Time series with the training samples.
learning_rate: Learning rate: scale the contribution of each tree by a factor of 0 < lr < 1 when it is added to the current approximation. Used to prevent overfitting. Default: 0.15
min_split_loss: Minimum loss reduction to make a further partition of a leaf. Default: 1.
max_depth: Maximum depth of a tree. Increasing this value makes the model more complex and more likely to overfit. Default: 5.
min_child_weight: If the leaf node has a minimum sum of instance weights lower than min_child_weight, tree splitting stops. The larger min_child_weight is, the more conservative the algorithm is. Default: 1.
max_delta_step: Maximum delta step we allow each leaf output to be. If the value is set to 0, there is no constraint. If it is set to a positive value, it can help making the update step more conservative. Default: 1.
subsample: Percentage of samples supplied to a tree. Default: 0.8.
nfold: Number of the subsamples for the cross-validation.
nrounds: Number of rounds to iterate the cross-validation (default: 100)
nthread: Number of threads (default = 6)
early_stopping_rounds: Training with a validation set will stop if the performance doesn't improve for k rounds.
verbose: Print information on statistics during the process

Value

Model fitted to input data (to be passed to sits_classify)

Note

Please refer to the sits documentation available in https://e-sensing.github.io/sitsbook/ for detailed examples.

References

Tianqi Chen, Carlos Guestrin, "XGBoost : Reliable Large-scale Tree Boosting System", SIG KDD 2016.

Author

Gilberto Camara, gilberto.camara@inpe.br

Examples

if (sits_run_examples()) {
    # Example of training a model for time series classification
    # Retrieve the samples for Mato Grosso
    # train a xgboost model
    ml_model <- sits_train(samples_modis_ndvi, ml_method = sits_xgboost)
    # classify the point
    point_ndvi <- sits_select(point_mt_6bands, bands = "NDVI")
    # classify the point
    point_class <- sits_classify(
        data = point_ndvi, ml_model = ml_model
    )
    plot(point_class)
}