sits (version 1.5.3)

sits_lightgbm: 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,
  ...
)

Value

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

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.

Author

Gilberto Camara,

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)
}