https://github.com/dms-codes/mean-squared-error

https://github.com/dms-codes/mean-squared-error

Last synced: 2 days ago
JSON representation

• Host: GitHub
• URL: https://github.com/dms-codes/mean-squared-error
• Owner: dms-codes
• Created: 2024-12-07T02:11:29.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-12-07T02:11:57.000Z (about 1 month ago)
• Last Synced: 2024-12-07T03:19:01.770Z (about 1 month ago)
• Language: Python
• Size: 1000 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Mean Squared Error (MSE) Calculation

This file demonstrates two ways to calculate the Mean Squared Error (MSE) between two lists of values:

1. **Manual Calculation:** 

   - The `mean_squared_error` function calculates the MSE directly by:

     - Checking for equal lengths of input lists.

     - Calculating the squared difference between corresponding values.

     - Summing the squared errors.

     - Dividing the sum of squared errors by the number of data points.

2. **Using scikit-learn:**

   - The `calculate_mse` function leverages the `mean_squared_error` function from the scikit-learn library for efficient and concise MSE calculation.

## Example Usage

Both functions can be used as follows:

```python

actual = [2, 3, 5, 5, 9]

calculated = [3, 3, 8, 7, 6]

# Manual Calculation

mse_manual = mean_squared_error(actual, calculated) 

print(f"Mean Squared Error (Manual): {mse_manual}")

# Using scikit-learn

mse_sklearn = calculate_mse(actual, calculated)

print(f"Mean Squared Error (scikit-learn): {mse_sklearn}")