My Notes on MAE vs MSE Error Metrics 🚀

Data Science Tech Brief By HackerNoon

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We will focus on MSE and MAE metrics, which are frequently used model evaluation metrics in regression models.
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We will focus on MSE and MAE metrics, which are frequently used model evaluation metrics in regression models. MAE is the average distance between the real data and the predicted data, but fails to punish large errors in prediction. MSE measures the average squared difference between the estimated values and the actual value. L1 and L2 Regularization is a technique used to reduce the complexity of the model. It does this by penalizing the loss function by regularizing the function of the function.