| sits_rfor {sits} | R Documentation |
Train random forest models
Description
Use Random Forest algorithm to classify samples.
This function is a front-end to the randomForest package.
Please refer to the documentation in that package for more details.
Usage
sits_rfor(samples = NULL, num_trees = 100, mtry = NULL, ...)
Arguments
samples |
Time series with the training samples (tibble of class "sits"). |
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: 100) (integer, min = 50, max = 150). |
mtry |
Number of variables randomly sampled as candidates at
each split (default: NULL - use default value of
|
... |
Other parameters to be passed to 'randomForest::randomForest' function. |
Value
Model fitted to input data
(to be passed to sits_classify).
Author(s)
Alexandre Ywata de Carvalho, alexandre.ywata@ipea.gov.br
Rolf Simoes, rolf.simoes@inpe.br
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 random forest model
rf_model <- sits_train(samples_modis_ndvi,
ml_method = sits_rfor
)
# classify the point
point_ndvi <- sits_select(point_mt_6bands, bands = "NDVI")
# classify the point
point_class <- sits_classify(
data = point_ndvi, ml_model = rf_model
)
plot(point_class)
}