sits_rfor: Train random forest models

In e-sensing/sits: Satellite Image Time Series Analysis for Earth Observation Data Cubes

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 randomForest::randomForest() function, i.e. floor(sqrt(features))).
...	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, [e-mail]

Rolf Simoes, [e-mail]

Gilberto Camara, [e-mail]

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(mtry = 20)
    )
    # 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)
}

e-sensing/sits documentation built on Jan. 28, 2024, 6:05 a.m.