rslearn-py 1.0.3

A simple sklearn-like ML library built from scratch

🚀 rslearn

A beginner-friendly machine learning library that automates preprocessing, training, and evaluation.

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NOTE

This is Python version of this library visit more.

• C++ Version

Origination - rslearn-lib

• rslearn-ML
• rslearn-lib

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✨ Why rslearn?

• ⚡ Minimal setup — no complex configuration
• 🤖 Automatic pipeline (scaling, splitting, evaluation)
• 📊 Built-in metrics for regression & classification
• 🧠 Designed for beginners learning ML concepts
• 🧩 Clean and simple API inspired by sklearn

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Release & Changes

• Version : 1.0.8 - 1.0.2
• Release Date: 2026-06-25

Latest (In Pipeline & linear_model):

• KNN Regression & Classification Both
• Evaluation & auto scaling support

More Info: CHANGELOG

Download Version Specific Module

Downloads - Module

📊 Linear Models

• Linear Regression (Single & Multi-feature)
• Logistic Regression (Binary & Multi-class)
• Ridge Regression (L2 Regularization)
• Lasso Regression (L1 Regularization)
• Elastic Net (L1 + L2)

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📏 Metrics

• Mean Squared Error (MSE)
• Mean Absolute Error (MAE)
• Root Mean Squared Error (RMSE)
• R² Score
• Accuracy (for classification)

✔ Supports single-output and multi-output tasks

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🔧 Preprocessing

• StandardScaler
• MinMaxScaler

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🧪 Model Selection

• Train-Test Split

  • Supports stratify for balanced sampling

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⚙️ Optimization Details

All models in rslearn are implemented using Gradient Descent.

⚠️ Important:

• Feature scaling is highly recommended for stable and faster convergence.

• Use:

  • StandardScaler (recommended)
  • or MinMaxScaler

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🤖 Auto Standard Scaling (Linear, Logistic, Ridge, Lasso, ElasticNet)

models include Inbuilt StandardScaler Feature in fit() Method:

scale=True  # default

• Automatically applies feature scaling internally
• Helps prevent numerical instability

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📁 Project Structure

rslearn/
│
├── linear_model/
│   ├── _linear_regression.py
│   ├── _logistic_regression.py
│   ├── _ridge.py
│   ├── _lasso.py
│   ├── _elastic_net.py
│
├── preprocessing/
│   ├── _scaler.py
│
├── metrics/
│   ├── _regression.py
│
├── model_selection/
│   ├── _split.py
│
└── README.md

📌 Each module contains its own detailed README with usage examples and explanations.

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🛠️ Installation

Clone the repository

git clone https://github.com/rslearn-lib/rslearn-ML-py.git
cd rslearn-ML-py/

Install Usable Library (Stable - Latest)

pip install rslearn-py

Download Version Specific Module

Downloads Older Library

Install dependencies

pip install -r requirements.txt

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📌 Quick Example

from rslearn.linear_model import LinearRegression
from rslearn.preprocessing import StandardScaler
import numpy as np

X = np.array([10, 20, 30])
y = np.array([5, 10, 15])

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

model = LinearRegression()
model.fit(X_scaled, y)

print(model.predict([40]))

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📚 Documentation

• Each folder includes its own README.md

• Covers:

  • Usage
  • Parameters
  • Examples
  • Internal working

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🎯 Goals of this Project

• Understand ML algorithms from scratch
• Build a sklearn-like API
• Create reusable and modular ML components
• Learn real-world ML system design
• Check Self Ability

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🧑‍💻 Author

ItzRustam

🔨 Origination

rslearn-lib

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📜 License

This project is licensed under the GNU GPL v3 License.