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LightGBM

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lightgbm.py

This module is intended to implement the LightGBM algorithm, a gradient boosting framework that uses tree-based learning algorithms. It is designed for distributed and efficient training of large datasets.

Usage

Import the LIGHTGBM class and configure it with the necessary parameters. Train the model on your dataset and use it for predictions.

Example: from engines.contentFilterEngine.traditional_ml_algorithms.lightgbm import LIGHTGBM model = LIGHTGBM() model.fit(X_train, y_train) predictions = model.predict(X_test)

Note
  • This file currently contains a placeholder and requires a full implementation of the LightGBM algorithm.
  • LightGBM is particularly useful for large datasets and can handle categorical features directly.