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
Gilberto Camara, gilberto.camara@inpe.br
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)
}