| sits_train {sits} | R Documentation |
Given a tibble with a set of distance measures,
returns trained models. Currenly, sits supports the following models:
'svm' (see sits_svm),
random forests (see sits_rfor),
multinomial logit (see sits_mlr) and its variants
'lasso' (see sits_mlr) and
'ridge' (see sits_mlr),
extreme gradient boosting (see sits_xgboost),
and different deep learning functions, including multi-layer perceptrons
(see sits_mlp), 1D convolution neural
networks sits_TempCNN,
and a deep Residual Network sits_ResNet.
sits_train(data, ml_method = sits_svm())
data |
Time series with the training samples. |
ml_method |
Machine learning method. |
Model fitted to input data
to be passed to sits_classify
Rolf Simoes, rolf.simoes@inpe.br
Gilberto Camara, gilberto.camara@inpe.br
Alexandre Ywata de Carvalho, alexandre.ywata@ipea.gov.br
# Retrieve the set of samples for Mato Grosso (provided by EMBRAPA)
# fit a training model (RFOR model)
samples <- sits_select(samples_modis_4bands, bands = c("NDVI"))
ml_model <- sits_train(samples, sits_rfor(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)