matrics-calculator 3.2.1

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).

matrics_calculator

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).

Python Ecosystem

matrics_calculator provides a lightweight and easy-to-use alternative for calculating key regression metrics, complementing existing libraries like scikit-learn. While scikit-learn offers a full suite of machine learning tools, matrics_calculator focuses solely on evaluation metrics, making it a useful option for quick analysis, custom workflows, or educational purposes. Its simplicity makes it accessible for users who need essential regression metrics without the overhead of a larger machine learning framework.

Features

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 in the Python Ecosystem

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.

Installation

$ pip install matrics_calculator

Usage

Here’s how to use the functions in this package:

1. Import the Package

from matrics_calculator.r2 import r2_score
from matrics_calculator.MAE import mean_absolute_error
from matrics_calculator.MSE import mean_squared_error
from matrics_calculator.MAPE import mean_absolute_percentage_error

2. Prepare Your Data Ensure you have two arrays:

   y_true: The actual target values

   y_pred: The predicted values from your regression model

   Example:

y_true = [100, 200, 300]
y_pred = [110, 190, 290]

3. Calculate Metrics Use the functions to evaluate your model:

# Calculate MAPE
mape = mean_absolute_percentage_error(y_true, y_pred)
print(f"MAPE: {mape:.2f}%")

# Calculate MAE
mae = mean_absolute_error(y_true, y_pred)
print(f"MAE: {mae:.2f}")

# Calculate MSE
mse = mean_squared_error(y_true, y_pred)
print(f"MSE: {mse:.2f}")

# Calculate R-squared
r2 = r2_score(y_true, y_pred)
print(f"R-squared: {r2:.2f}")

Contributing

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.

License

matrics_calculator was created by Celine Habashy, Jay Mangat, Yajing Liu, Zhiwei Zhang. It is licensed under the terms of the MIT license.

Credits

matrics_calculator was created with cookiecutter and the py-pkgs-cookiecutter template.

Constributors

• Celine Habashy
• Jay Mangat
• Yajing Liu
• Zhiwei Zhang