sits (version 0.13.0-3)

sits_xgboost: Train a model with an extreme gradient boosting machine

Description

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(
  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
)

Arguments

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

Value

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

References

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

Examples

# 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"))
# }
# NOT RUN {
# }