LGBM (Light Gradient Boosting Machine) is a gradient boosting framework that uses tree-based learning algorithms. It's designed to be efficient, fast, and high-performance. LGBM is particularly well-suited for large datasets and complex machine learning tasks.\
- Histogram-based algorithm: LGBM uses histograms to approximate the distribution of data, significantly reducing memory usage and computation time.
- Exclusive feature bundling: This technique merges features with similar values, further improving computational efficiency.
- Categorical feature support: LGBM can directly handle categorical features without requiring one-hot encoding.
- Gradient-based one-side sampling (GOSS): GOSS focuses on data points with high gradients, improving training speed and generalization performance.
- Exclusive feature importance: This feature provides insights into the importance of each feature in the model.
- Python: Ensure you have Python 3.7 or later installed.
- Streamlit: Install Streamlit using pip
- Open a terminal or command prompt.
- Navigate to the directory where your app file is located.
- Run the following command - streamlit run app.py