Mean Sqaured Error, Mean Absolute Error And RMSE In Hindi- Linear Regression | Krish Naik Hindi

The Mean absolute error represents the average of the absolute difference between the actual and predicted values in the dataset. It measures the average of the residuals in the dataset.
Mean Squared Error represents the average of the squared difference between the original and predicted values in the data set. It measures the variance of the residuals.
Root Mean Squared Error is the square root of Mean Squared error. It measures the standard deviation of residuals.
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