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## Evaluation Metric for Regression

Regression analysis is a machine learning type which helps to find the relationship between independent variable and dependent variable. It can predict discrete values like price, rating, salary etc.

A machine learning model cannot be 100% efficient; otherwise, it is referred to as a biassed model. This also encompasses the concepts of overfitting and underfitting.

It is mandatory to determine the accuracy on the train data as well as important to check the accuracy on unseen data otherwise the Model is of no use. Evaluation of Machine learning on different metrics which helps us to better understand the performance of the model for better results. If one metric is perfect on the model, there is no need for multiple metrics.

• Mean Absolute Error (MAE)

• Mean Squared Error (MSE)

• Root Mean Squared Error (RMSE)

Mean Absolute Error (MAE) : The mean squared error is the most widely used regression measure. It effectively calculates the average of the squared difference between the target value and the regression model's predicted value. Where:

• y_j: original value

• y_hat: predicted value from the regression model

• N: number of datums Mean Absolute Error (MAE) : The average of the difference between the Original value and predicted values is the Mean Absolute Error. It is expressed mathematically as: Where:

• y_j: orignal value value

• y_hat: predicted value from the regression model

• N: number of datums Root Mean Squared Error (RMSE) : The square root of the average of the squared difference between the target value and the value predicted by the regression model is the Root Mean Squared Error. It is sqrt (MSE). It can be expressed mathematically as: Where:

• y_j: ground-truth value

• y_hat: predicted value from the regression model

• N: number of datums

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