DLCV 0.1

A package for cross-validation of dual leaf models

dual_leaf_cross_score

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
• Usage
  • Basic Usage
  • Advanced Usage
• Command-Line Interface (CLI)
• Development
• Testing
• Contributing
• License
• 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:

pip install DLCV