Regression-Metric-Library

** What metric does this code calculate?**

This code defines a Custom Score for evaluating a regression model, which is a combination of two metrics:

1. ( R^2 ) (coefficient of determination)

• Indicates how much of the variance in the actual data the model explains.
• The value is between 0 and 1 (the higher, the better).

2. ( MAPE ) (Mean Absolute Percentage Error)

• Indicates the relative error of the model.
• The value is between 0 and 1 (the lower, the better).

Formula used in the code:

• The value ( 1 - MAPE ) is used to reverse the effect of MAPE, so that a lower value gives a better score.
• Finally, the average of the two values ​​is taken and multiplied by 100 to give the output as a percentage.

Conclusion

✅ This class now calculates a metric. ✅ It can be used to analyze various regression models. ✅ The output can be easily prepared as a report.

🚀 This method is useful for creating a standard library in machine learning!

For example