🚀 mymodels : Assemble an efficient interpretable machine learning workflow

Feel free to contact me: gtzjh86@outlook.com

4/2/2025: Support for LabelEncoder, TargetEncoder, and FrequencyEncoder is under developing.

🤔 Why mymodels?

Interpretable machine learning has gained significant prominence across various fields. Machine learning models are valued for their robust capability to capture complex relationships within data through sophisticated fitting algorithms. Complementing these models, interpretability frameworks provide essential tools for revealing such "black-box" models. These interpretable approaches deliver critical insights by ranking feature importance, identifying nonlinear response thresholds, and analyzing interaction relationships between factors.

Project mymodels, is targeting on building a tiny, user-friendly, and efficient workflow, for the scientific researchers and students who are seeking to implement interpretable machine learning in their their research works.

👨‍🎓 Prerequisites for Beginners

1. Python Proficiency

   • Python tutorial on W3SCHOOL

   • Liao Xuefeng's Python Tutorial

   DO REMEMBER: Make a practical demo project after you finish the above learning to enhance what you have learned (i.e., a tiny web crawler). Here is one of my practice projects

2. Machine Learning Fundamentals

   • Stanford CS229 provides essential theory.

3. Technical Skills

   • Environment management with conda/pip
   • Terminal/Command Line proficiency
   • Version control with Git (My note about Git)

The above recommended tutorials are selected based solely on personal experience.

🛠️ Environment Setup

Supported platforms:

• Windows (X86) - Tested on Windows 10/11
• Linux (X86) - Tested on WSL2.0 (Ubuntu)
• macOS (ARM) - Tested on Apple Silicon (M1)

Requirements:

• Python 3.10.X

Create environment

conda env create -f requirement.yml -n mymodels -y

Activate

conda activate mymodels

👉 Try

Try the Titanic demo first

• Binary classification: run_titanic.ipynb

  Dataset source: Titanic: Machine Learning from Disaster

And then try other demos

• Multi-class classification: run_obesity.ipynb

  Dataset source: Obesity Risk Dataset

• Regression task: run_housing.ipynb

  Dataset source: Kaggle Housing Data

🤔 Something You may Interested in

Supported encode methods

Supported models

Some thing you should know before implementation