MachineLite

MachineLite provides a streamlined solution for evaluating various supervised machine learning algorithms, simplifying the data scientist's workflow by automating model integration and evaluation. With just a few lines of code, SciLite enables quick access to essential machine learning evaluations, saving valuable time and effort.

Installation

To install the package, run:

pip install machinelite

Usage

Basic example of how to use the package in Python:

from machinelite.univariate.regression import Regression as reg
result = reg(X_train, X_test, y_train, y_test)
print(result)

Features

• One-Click Model Integration: Supports easy integration and evaluation of multiple supervised machine learning models, whether for regression or classification.
• Efficient Model Evaluation: Simplifies machine learning model evaluations with standardized output, aiding in faster analysis and comparisons.

Contributing

Contributions to MachineLite are warmly welcomed! If you'd like to contribute, please fork the repository and submit a pull request or open an issue to discuss improvements.

Documentation

MachineLite: View Here

License

Contact

For any questions or issues, feel free to reach out to:

• Author: Ayush Jain Sparsh
• Email: ayushjainsparsh2004.ajs@gmail.com