semvec/pypi-packages huggingface dataset:
DLCV 0.1
A package for cross-validation of dual leaf models
Requires Python: >=3.5

# dual_leaf_cross_score

![dual_leaf_cross_score](https://media.licdn.com/dms/image/v2/D4D0BAQGn8qNS6_QvUQ/company-logo_200_200/company-logo_200_200/0/1721242987617/sinindia_logo?e=1738195200&v=beta&t=mox2b3mqquER99-D4oqTR67DsCqWlf5yriX2QPjixZ0) <!-- Replace with actual image URL -->

`dual_leaf_cross_score` is an advanced Python package designed to simplify and optimize the cross-validation process for machine learning models. While its architecture is tailored for dual leaf models, it remains highly flexible and can be used with any estimator that implements the `fit` method (e.g., scikit-learn models).

This package was proudly developed by **SIN Technologies Private Limited**, as part of our mission to deliver highly scalable and efficient AI/ML solutions.

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## Key Features

- **Cross-Validation Simplified**: Perform model evaluation using a variety of cross-validation techniques like `KFold`, `StratifiedKFold`, or custom splitting strategies.
- **Dual Leaf Models Focus**: Special optimizations for dual leaf models, enhancing accuracy and speed in scoring.
- **Custom Metrics**: Easily define custom scoring metrics or use the default `mean` score for fast evaluation.
- **Broad Compatibility**: Works seamlessly with scikit-learn pipelines, supporting classifiers, regressors, and more.
- **CLI Support**: You can execute cross-validation tasks directly from the command line, offering flexibility for scripting or integration into larger workflows.

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## Table of Contents

- [Installation](#installation)
- [Usage](#usage)
  - [Basic Usage](#basic-usage)
  - [Advanced Usage](#advanced-usage)
- [Command-Line Interface (CLI)](#command-line-interface-cli)
- [Development](#development)
- [Testing](#testing)
- [Contributing](#contributing)
- [License](#license)
- [Acknowledgments](#acknowledgments)

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## Installation

### Prerequisites

Ensure you have Python 3.6+ installed on your system. The package depends on several libraries, including scikit-learn and numpy. If you're using a virtual environment, ensure it's activated before proceeding.

### Installing via PyPI

To install the latest version of `dual_leaf_cross_score` from [PyPI](https://pypi.org/project/dual_leaf_cross_score):

```bash
pip install DLCV