| sits_xgboost {sits} | R Documentation |
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( data = 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 )
data |
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) |
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 |
Model fitted to input data
(to be passed to sits_classify)
Alexandre Ywata de Carvalho, alexandre.ywata@ipea.gov.br
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.
## Not run:
# Retrieve the set of samples for Mato Grosso (provided by EMBRAPA)
# Build a machine learning model based on xgboost
xgb_model <- sits_train(samples_modis_4bands, sits_xgboost(nrounds = 10))
# get a point and classify the point with the ml_model
point.tb <- sits_select(point_mt_6bands,
bands = c("NDVI", "EVI", "NIR", "MIR")
)
class.tb <- sits_classify(point.tb, xgb_model)
plot(class.tb, bands = c("NDVI", "EVI"))
## End(Not run)