Metadata-Version: 2.4
Name: xgboost-tuner-pack
Version: 0.1.1
Summary: A world-class hyperparameter tuner for XGBoost.
Home-page: https://github.com/AroopGit/XGB_Tuner
Author: Aroop
Author-email: work.arooprath@gmail.com
Keywords: xgboost tuner hyperparameter optimization
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Requires-Dist: xgboost>=1.0
Requires-Dist: scikit-learn>=1.0
Requires-Dist: numpy>=1.17
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Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
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Dynamic: keywords
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# xgbtuner

[![PyPI version](https://img.shields.io/pypi/v/xgbtuner.svg)](https://pypi.org/project/xgbtuner)  
[![Python versions](https://img.shields.io/pypi/pyversions/xgbtuner.svg)]()  
[![License](https://img.shields.io/pypi/l/xgbtuner.svg)](LICENSE)

A **world-class**, **easy-to-use** hyperparameter tuner for XGBoost models, built on top of scikit-learn’s `GridSearchCV` and `RandomizedSearchCV`. Supports both **classification** and **regression** out of the box, with robust error handling, comprehensive testing, and flexible customization.

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## 🚀 Features

- **Grid Search & Random Search**  
  Choose between exhaustive grid search or randomized search for hyperparameter exploration.
- **Classification & Regression**  
  One API for both objectives—simply set `objective="classification"` or `"regression"`.
- **Default & Custom Grids**  
  Sensible default parameter grid, plus ability to pass your own `param_grid`.
- **Robust Input Validation**  
  Checks for array-like inputs, consistent lengths, and raises clear errors.
- **Detailed Logging**  
  Built-in `logging` statements to trace tuning progress and errors.
- **Fully Tested**  
  Over 10 unit tests cover edge cases, custom grids, list inputs, invalid configs, and more.
- **Scikit-Learn Compatible**  
  Behaves like any estimator: `.tune()`, `.predict()`, and `.predict_proba()` for classification.

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## 📦 Installation

Install from PyPI:

```bash
pip install xgbtuner
