sits_lightgbm: Train light gradient boosting model

In sits: Satellite Image Time Series Analysis for Earth Observation Data Cubes

Train light gradient boosting model

Description

Use LightGBM algorithm to classify samples. This function is a front-end to the lightgbm package. LightGBM (short for Light Gradient Boosting Machine) is a gradient boosting framework developed by Microsoft that's designed for fast, scalable, and efficient training of decision tree-based models. It is widely used in machine learning for classification, regression, ranking, and other tasks, especially with large-scale data.

Usage

sits_lightgbm(
  samples = NULL,
  boosting_type = "gbdt",
  objective = "multiclass",
  min_samples_leaf = 20,
  max_depth = 6,
  learning_rate = 0.1,
  num_iterations = 100,
  n_iter_no_change = 10,
  validation_split = 0.2,
  ...
)

Arguments

samples	Time series with the training samples.
boosting_type	Type of boosting algorithm (default = "gbdt")
objective	Aim of the classifier (default = "multiclass").
min_samples_leaf	Minimal number of data in one leaf. Can be used to deal with over-fitting.
max_depth	Limit the max depth for tree model.
learning_rate	Shrinkage rate for leaf-based algorithm.
num_iterations	Number of iterations to train the model.
n_iter_no_change	Number of iterations without improvements until training stops.
validation_split	Fraction of the training data for validation. The model will set apart this fraction and will evaluate the loss and any model metrics on this data at the end of each epoch.
...	Other parameters to be passed to 'lightgbm::lightgbm' function.

Value

Model fitted to input data (to be passed to sits_classify).

Author(s)

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
    lgb_model <- sits_train(samples_modis_ndvi,
        ml_method = sits_lightgbm
    )
    # classify the point
    point_ndvi <- sits_select(point_mt_6bands, bands = "NDVI")
    # classify the point
    point_class <- sits_classify(
        data = point_ndvi, ml_model = lgb_model
    )
    plot(point_class)
}

sits documentation built on Sept. 9, 2025, 5:54 p.m.