| Name | Mean-Squared-Error JSON |
| Version |
0.4.0
JSON |
| download |
| home_page | None |
| Summary | None |
| upload_time | 2025-02-23 16:53:03 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | None |
| license | None |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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No Travis.
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# MSE - Mean Squared Error Calculation Package
## Overview
**Mean_Squared_Error** is a Python package for calculating the Mean Squared Error (MSE), a common metric for evaluating regression models. This package provides a simple and efficient way to compute MSE for model predictions.
## Installation
You can install **Mean_Squared_Error** using pip:
```bash
pip install Mean_Squared_Error
```
## Usage
```python
from Mean_Squared_Error import MSE
# Example usage
result = MSE([1, 2, 3], [4, 5, 6])
print(result)
```
### MSE(y_true, y_pred)
Calculates the Mean Squared Error between true values and predicted values.
**Parameters:**
- `y_true` (list/array): Ground truth values
- `y_pred` (list/array): Predicted values
**Returns:**
- float: The calculated Mean Squared Error
## Examples
```python
# Basic usage
true_values = [1, 2, 3, 4, 5]
predicted_values = [1.1, 2.2, 2.9, 4.1, 5.2]
error = MSE(true_values, predicted_values)
print(f"Mean Squared Error: {error}")
# Using with numpy arrays
import numpy as np
y_true = np.array([1.0, 2.0, 3.0])
y_pred = np.array([1.1, 1.9, 3.2])
error = MSE(y_true, y_pred)
print(f"Mean Squared Error: {error}")
```
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
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"description": "# MSE - Mean Squared Error Calculation Package\r\n\r\n## Overview\r\n\r\n**Mean_Squared_Error** is a Python package for calculating the Mean Squared Error (MSE), a common metric for evaluating regression models. This package provides a simple and efficient way to compute MSE for model predictions.\r\n\r\n## Installation\r\n\r\nYou can install **Mean_Squared_Error** using pip:\r\n\r\n```bash\r\npip install Mean_Squared_Error\r\n```\r\n\r\n## Usage\r\n\r\n```python\r\nfrom Mean_Squared_Error import MSE\r\n\r\n# Example usage\r\nresult = MSE([1, 2, 3], [4, 5, 6])\r\nprint(result)\r\n```\r\n\r\n\r\n### MSE(y_true, y_pred)\r\n\r\nCalculates the Mean Squared Error between true values and predicted values.\r\n\r\n**Parameters:**\r\n- `y_true` (list/array): Ground truth values\r\n- `y_pred` (list/array): Predicted values\r\n\r\n**Returns:**\r\n- float: The calculated Mean Squared Error\r\n\r\n## Examples\r\n\r\n```python\r\n# Basic usage\r\ntrue_values = [1, 2, 3, 4, 5]\r\npredicted_values = [1.1, 2.2, 2.9, 4.1, 5.2]\r\nerror = MSE(true_values, predicted_values)\r\nprint(f\"Mean Squared Error: {error}\")\r\n\r\n# Using with numpy arrays\r\nimport numpy as np\r\ny_true = np.array([1.0, 2.0, 3.0])\r\ny_pred = np.array([1.1, 1.9, 3.2])\r\nerror = MSE(y_true, y_pred)\r\nprint(f\"Mean Squared Error: {error}\")\r\n```\r\n\r\n## Contributing\r\n\r\nContributions are welcome! Please feel free to submit a Pull Request.\r\n",
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