R: Train classification models

sits_train {sits} | R Documentation

## Train classification models

### Description

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`.

### Usage

    sits_train(data, ml_method = sits_svm())

### Arguments

`data` | Time series with the training samples.
`ml_method` | Machine learning method.

### Value

Model fitted to input data to be passed to `sits_classify`

### Author(s)

Rolf Simoes, rolf.simoes@inpe.br

Gilberto Camara, gilberto.camara@inpe.br

Alexandre Ywata de Carvalho, alexandre.ywata@ipea.gov.br

### 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 = 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)

[Package sits version 0.16.2 Index]