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| Topic | Description |
|---|---|
| Hyperparameter Tuning | Optimizing hyperparameters for LightGBM models, including learning rate, tree depth, and more. |
| Handling Imbalanced Data | Strategies to address imbalanced datasets, such as oversampling, undersampling, and specialized LightGBM techniques. |
| Feature Engineering | Techniques for feature selection, transformation, and engineering to enhance the performance of LightGBM models. |
| Time Series Forecasting | Application of LightGBM in time series forecasting with specific techniques and parameter adjustments. |
| Interpreting LightGBM Models | Understanding model predictions, feature importance, and utilizing interpretation techniques and visualizations. |
| Advanced Techniques | Handling advanced LightGBM methods like categorical features, early stopping, custom loss functions, and metrics. |
| Large Datasets | Efficiently working with large datasets and optimizing LightGBM for performance in such scenarios. |
| Ensemble Learning | Combining multiple LightGBM models into ensembles for improved performance and understanding ensemble techniques. |
| GPU Acceleration | Utilizing GPU acceleration for faster training of LightGBM models, including setup and usage guidance. |
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