if (sits_run_examples()) {
# select a set of samples
samples <- samples_modis_ndvi
# index samples to split train/test
samples[["sample_idx"]] <- 1:nrow(samples)
# select training data
train_data <- sits_sample(samples, frac = 0.8)
# select test data
sel <- !(samples[["sample_idx"]]
%in% train_data[["sample_idx"]])
test_data <- samples[sel, ]
# compute a random forest model
rfor_model <- sits_train(train_data, sits_rfor())
# classify training points
points_class <- sits_classify(
data = test_data, ml_model = rfor_model
)
# calculate accuracy
acc <- sits_accuracy(points_class)
# plot accuracy
plot(acc)
}
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