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# What is a loss function? Sarvech Qadir

Loss functions are used to estimate and understand how well a particular algorithm performs. They are classified into two categories:

1. Regression Models are used to predict continuous values.

2. Classification Models are used to predict the particular output from a set of finite and categorical values. ## Regression losses

### Mean Squared Error (MSE)

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.  ### Mean Absolute Error (MAE)

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.  ### Mean Bias Error (MBE)

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

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.  ## Classification losses

### Cross Entropy Loss

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

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.  RELATED TAGS

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CONTRIBUTOR Sarvech Qadir 