How do you measure the accuracy of a linear regression model?

For linear regression, accuracy is usually measured by how close the predicted values are to the actual values, using error metrics (since it predicts continuous values).

Here are the most common ones:

1. R-squared (R²) :- It is a number between 0 and 1 that tells you how well your regression model explains the variation in the target variable.
   1. R² = 1 → Perfect fit (model explains 100% of the variation)
   2. R² = 0 → Model explains nothing (predictions are no better than the mean)
2. Mean Absolute Error (MAE)
3. Mean Squared Error (MSE)
4. Root Mean Squared Error (RMSE)