1. Mean Absolute Error (MAE): This is the simplest and most common used metric for regression evaluation. It is the sum of absolute differences between our target and predicted variables. It measures the average magnitude of errors in a set of predictions, without considering their direction.

2. Mean Squared Error (MSE): This metric is similar to MAE but squares the difference before summing them all instead of using the absolute value. The squaring is particularly useful if you want to penalize large errors.

3. Root Mean Squared Error (RMSE): This is the square root of the mean of the square of all of the error. RMSE is even more popular than MSE, because RMSE is interpretable in the "y" units. It measures the standard deviation of the residuals.

These are the three common evaluation metrics for regression. Each has its own advantages and disadvantages, and are used in different scenarios depending on the specific requirements of your regression model.

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1. Mean Absolute Error (MAE): This is the simplest and most common used metric for regression evaluation. It is the sum of absolute differences between our target and predicted variables. It measures the average magnitude of errors in a set of predictions, without considering their direction.

2. Mean Squared Error (MSE): This metric is similar to MAE but squares the difference before summing them all instead of using the absolute value. The squaring is particularly useful if you want to penalize large errors.

3. Root Mean Squared Error (RMSE): This is the square root of the mean of the square of all of the error. RMSE is even more popular than MSE, because RMSE is interpretable in the "y" units. It measures the standard deviation of the residuals.

These are the three common evaluation metrics for regression. Each has its own advantages and disadvantages, and are used in different scenarios depending on the specific requirements of your regression model.

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1. Mean Absolute Error (MAE): This is the simplest and most common used metric for regression evaluation. It is the sum of absolute differences between our target and predicted variables. It measures the average magnitude of errors in a set of predictions, without considering their direction.

2. Mean Squared Error (MSE): This metric is similar to MAE but squares the difference before summing them all instead of using the absolute value. The squaring is particularly useful if you want to penalize large errors.

3. Root Mean Squared Error (RMSE): This is the square root of the mean of the square of all of the error. RMSE is even more popular than MSE, because RMSE is interpretable in the "y" units. It measures the standard deviation of the residuals.

These are the three common evaluation metrics for regression. Each has its own advantages and disadvantages, and are used in different scenarios depending on the specific requirements of your regression model.

The three common evaluation metrics for regression are

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