Monotonic composite quantile gradient boost regressor
Project description
MQBoost introduces an advanced model for estimating multiple quantiles while ensuring the non-crossing condition (monotone quantile condition). This model harnesses the capabilities of both LightGBM and XGBoost, two leading gradient boosting frameworks.
By implementing the hyperparameter optimization prowess of Optuna, the model achieves great performance. Optuna's optimization algorithms fine-tune the hyperparameters, ensuring the model operates efficiently.
Installation
Install using pip:
pip install mqboost
Usage
Features
- MQDataset: Encapsulates the dataset used for MQRegressor and MQOptimizer.
- MQRegressor: Custom multiple quantile estimator with preserving monotonicity among quantiles.
- MQOptimizer: Optimize hyperparameters for MQRegressor with Optuna.
Example
Please refer to the Examples provided for further clarification.
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