cherrypick-ml 0.1.0

A lightweight ML orchestration library with preprocessing, anomaly detection, and explainability tools

---

cherrypick-ml: A Machine Learning Orchestration and Pipeline Toolkit

Testing	Structured validation of preprocessing, orchestration, and explainability components
Package	PyPI distribution for cherrypick-ml
Meta	MIT License, Python-based machine learning pipeline framework

---

What is it?

cherrypick-ml is a Python package that provides a unified interface for building, managing, and evaluating machine learning workflows. It integrates preprocessing, anomaly detection, model orchestration, and explainability into a single, modular framework.

The library is designed to simplify real-world machine learning development by reducing repetitive code while maintaining flexibility and transparency in model pipelines.

---

Table of Contents

• Main Features
• Core Components
• Where to get it
• Dependencies
• Installation from sources
• Basic Usage
• License
• Documentation

---

Main Features

cherrypick-ml provides the following core capabilities:

• Automated model orchestration for classification and regression tasks
• Integrated preprocessing utilities including encoding and missing value handling
• Outlier detection using statistical method such as Inter quartile range(IQR), Z-score, modified Z-score, Isolation Forest and Local Outlier Factor based outlier pruning
• SHAP-based explainability for feature importance and model interpretation
• Flexible train-test splitting utilities
• Modular design allowing independent usage of components
• Designed for practical, real-world machine learning workflows

---

Core Components

The library is structured into the following modules:

• Orchestrator
  High-level interface for training, evaluating, and selecting models with explainable visualisation

• preprocessing
  Tools for encoding, imputation, and feature preparation

• anomaly
  Outlier detection and data pruning utilities

• explain
  Model explainability using SHAP-based analysis

• splits
  Utilities for dataset partitioning

---

Where to get it

The source code is currently hosted on GitHub at:

https://github.com/Sujal-G-Sanyasi/cherrypick-ml

Binary installers for the latest released version are available at the Python Package Index (PyPI):

pip install cherrypick-ml