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.
sits_xgboost(
samples = NULL,
learning_rate = 0.15,
min_split_loss = 1,
max_depth = 5,
min_child_weight = 1,
max_delta_step = 1,
subsample = 0.8,
nfold = 5,
nrounds = 100,
early_stopping_rounds = 20,
verbose = FALSE
)Model fitted to input data
(to be passed to sits_classify)
Time series with the training samples.
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
Minimum loss reduction to make a further partition of a leaf. Default: 1.
Maximum depth of a tree. Increasing this value makes the model more complex and more likely to overfit. Default: 5.
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.
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.
Percentage of samples supplied to a tree. Default: 0.8.
Number of the subsamples for the cross-validation.
Number of rounds to iterate the cross-validation (default: 100)
Training with a validation set will stop if the performance doesn't improve for k rounds.
Print information on statistics during the process
Rolf Simoes, rolf.simoes@inpe.br
Gilberto Camara, gilberto.camara@inpe.br
Tianqi Chen, Carlos Guestrin, "XGBoost : Reliable Large-scale Tree Boosting System", SIG KDD 2016.
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(point_ndvi, ml_model)
plot(point_class)
}
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