🚀 rslearn

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

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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
• 📈 Automated evaluations with evaluate_model

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Release & Changes

• Version : 1.1.0 - 1.0.4
• Release Date: 2026-06-26
• CHANGELOG

NOTE

tests/* will be changed for each functions after documantation update.

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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📊 K-nearest Neighbors Models

• KNNRegressor (Single & Multi-feature)
• KNNClassifier (Binary & Multi-class)

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📏 Metrics

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

✔ 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

• or just use scale=True parameter while fit()

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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-ML/
│
├── rslearn/
│   │
│   ├── BaseEstimators/
│   │   └── __init__.py  
│   │   └── _base.py  
│   │   └── _estimator.py  
│   │
│   ├── Errors/
│   │   ├── __init__.py
│   │   └── _errors.py  
│   │
│   ├── loader/
│   │   ├── __init__.py
│   │   └── _pipeline_loader.py
│   │   └── _model_loader.py  
│   │
│   ├── linear_model/
│   │   ├── __init__.py
│   │   ├── README.md
│   │   ├── _LinearRegression.py
│   │   ├── _LogisticRegression.py
│   │   └── _regulizations.py
│   │
│   ├── metrics/
│   │   ├── __init__.py
│   │   ├── README.md  
│   │   ├── _evaluations.py  
│   │   ├── regression_readme.md  
│   │   ├── evaluation.md  
│   │   ├── classification_readme.md
│   │   ├── _classification.py
│   │   ├── _distances.py
│   │   └── _regression.py
│   │
│   ├── model_selection/
│   │   ├── __init__.py
│   │   ├── README.md
│   │   └── _split.py
│   │
│   ├── neighbors/
│   │   ├── __init__.py
│   │   ├── README.md
│   │   ├── _knnClass.py
│   │   └── _knnReg.py
│   │
│   ├── Pipeline/
│   │   ├── __init__.py
│   │   ├── README.md
│   │   └── _pipeline.py
│   │
│   ├── preprocessing/
│   │   ├── __init__.py
│   │   ├── README.md
│   │   └── _scaler.py
│   │
│   └── __init__.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

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

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

model = LinearRegression()
model.fit(X, y, scale=True) # Auto Scale if True, else Gradient Stability Backend

print(model.predict([40]))

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📚 Documentation

• Each folder includes its own README.md

• Covers:

  • Usage
  • Parameters
  • Examples
  • Internal working
    Good For Developers & Contributors

User Guide

• Docs
• user guide
• quick start guide

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🧑‍💻 Author

ItzRustam

🔨 Origination

rslearn-lib

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📜 License

This project is licensed under the GNU GPL v3 License.