| sits_ranger {sits} | R Documentation |
Use Fast Random Forest algorithm to classify data. This function is a front-end to the "ranger" method in the "ranger" package. Please refer to the documentation in that package for more details.
sits_ranger(data = NULL, num_trees = 2000, importance = "impurity", ...)
data |
Time series with the training samples. |
num_trees |
Number of trees to grow. This should not be set to too small a number, to ensure that every input row gets predicted at least a few times. (default: 2000). |
importance |
Variable importance mode, one of 'none', 'impurity', 'impurity_corrected', 'permutation'. The 'impurity' measure is the Gini index. |
... |
Other |
Model fitted to input data
(to be passed to sits_classify)
Gilberto Camara, gilberto.camara@inpe.br
Alexandre Ywata de Carvalho, alexandre.ywata@ipea.gov.br
Rolf Simoes, rolf.simoes@inpe.br
# Retrieve the set of samples for Mato Grosso (provided by EMBRAPA)
samples_ndvi <- sits_select(samples_mt_6bands, bands = c("NDVI"))
# Build a machine learning model
ml_model <- sits_train(samples_ndvi, sits_ranger(num_trees = 100))
# get a point and classify the point with the ml_model
point_ndvi <- sits_select(point_mt_6bands, bands = "NDVI")
class <- sits_classify(point_ndvi, ml_model)