sits (version 1.1.0)

sits_train: Train classification models

Description

Given a tibble with a set of distance measures, returns trained models. Currently, sits supports the following models: 'svm' (see sits_svm), random forests (see sits_rfor), extreme gradient boosting (see sits_xgboost), and different deep learning functions, including multi-layer perceptrons (see sits_mlp), 1D convolution neural networks sits_tempcnn, deep residual networks sits_resnet and self-attention encoders sits_lighttae

Usage

sits_train(samples, ml_method = sits_svm())

Value

Model fitted to input data to be passed to sits_classify

Arguments

samples

Time series with the training samples.

ml_method

Machine learning method.

Author

Rolf Simoes,

Gilberto Camara,

Alexandre Ywata de Carvalho,

Examples

# 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 = 50))
# 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)