Loss functions are used to estimate and understand how well a particular algorithm performs. They are classified into two categories:
Regression Models are used to predict continuous values.
Classification Models are used to predict the particular output from a set of finite and categorical values.
MSE is also referred to as quadratic or L2 loss. This method calculates the mean(average) of the square of the difference between predictions and actual observations.
MAE is also referred to as L1 loss. This method calculates the mean(average) of the sum of the absolute differences between predictions and actual observations.
MBE is similar to MSE but less accurate. However, it is used to conclude if the model’s bias was negative or positive.
Huber Loss is also referred to as Smooth Mean Absolute Error. This is an absolute error and often becomes a quadratic error when the error is too tiny. However, it is not that sensitive to outliers.
Cross Entropy Loss is also referred to as Negative Log Likelihood or Log Loss. It measures a classification model’s performance where the output is a probability value between 0 and 1. Cross-entropy loss increases as the predicted probability diverges from the actual label.
Hinge Loss is also referred to as a multi-class SVM loss. Hinge loss is applied for maximum-margin classification, most prominently for support vector machines (SVMs). It is used for training classifiers and is a convex function used in convex optimizers.
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