A package providing functions to calculate key regression metrics: R-squared, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE).
This package consists of four functions:
r_squared:- This function calculates the R-squared of the model, which measures how well the model explains the variation in the data.
mean_absolute_error:- This function finds the average difference between predicted and actual values.
mean_squared_error:- This function calculates the average of the squared differences between predictions and actual values.
mean_absolute_percentage_error:- This function shows prediction error as a percentage, making it easy to understand.
matrics_calculator works alongside Python libraries like scikit-learn by providing simple implementations of regression metrics. Unlike scikit-learn’s full toolkit for modeling and evaluation, this package focuses only on metrics, making it easy to use for quick analysis or custom workflows.
$ pip install matrics_calculator- TODO
Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.
matrics_calculator was created by Celine Habashy, Jay Mangat, Yajing Liu, Zhiwei Zhang. It is licensed under the terms of the MIT license.
matrics_calculator was created with cookiecutter and the py-pkgs-cookiecutter template.
- Celine Habashy
- Jay Mangat
- Yajing Liu
- Zhiwei Zhang