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**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](https://github.com/microsoft/LightGBM) and [XGBoost](https://github.com/dmlc/xgboost), two leading gradient boosting frameworks.

By implementing the hyperparameter optimization prowess of [Optuna](https://github.com/optuna/optuna), the model achieves great performance. Optuna's optimization algorithms fine-tune the hyperparameters, ensuring the model operates efficiently.

# Installation
Install using pip:
```bash
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**](https://github.com/RektPunk/MQBoost/tree/main/examples) provided for further clarification.


Monotonic composite quantile gradient boost regressor. 

Meet the Socket Team at BlackHat and DEF CON in Las Vegas, Aug 4-6

mqboost

Monotonic composite quantile gradient boost regressor

rektpunk

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mqboost

Installation

Usage

Features

Example

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