Loss Functions

Introduction

Loss function (or cost function) is widely used for measuring the differences between predicted goal and real value. In different scenario, user should choose different loss function. This blog will introduce some markable loss function and their usage conditions and disadvantages.

Log Loss

Focal Loss

KL Divergence / Relative Entropy

Exponential Loss

Hinge Loss

Mean Square Error

Quadratic Loss

$$ \lambda(x) = C (t-x)^2 $$

Quadratic Loss Function - Wikipedia


Huber Loss / Smooth Mean Absolute Error

Huber Loss和Focal Loss的原理与实现


Log Cosh Loss

Quantile Loss

MSE

Mean Squared Error (MSE), also known as mean squared deviation (MSD) in statics.

Mean Square Error - Wikipedia


MAE

Mean Absolute Error (MAE)

Mean Absolute Error - Wikipedia


Cross Entropy


Focal Loss

Introduction

References

Huber Loss和Focal Loss的原理与实现

Total References

Huber Loss和Focal Loss的原理与实现
Mean Square Error - Wikipedia
Quadratic Loss Function - Wikipedia
Mean Absolute Error - Wikipedia

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