[source] # Huber Loss The pseudo Huber Loss function transitions between L1 and L2 loss at a given pivot point (defined by *delta*) such that the function becomes more quadratic as the loss decreases. The combination of L1 and L2 losses make Huber more robust to outliers while maintaining smoothness near the minimum. $$ L_{\delta}= \left\{\begin{matrix} \frac{1}{2}(y - \hat{y})^{2} & if \left | (y - \hat{y}) \right | < \delta\\ \delta ((y - \hat{y}) - \frac1 2 \delta) & otherwise \end{matrix}\right. $$ ## Parameters | # | Name | Default | Type | Description | |---|---|---|---|---| | 1 | delta | 1.0 | float | The pivot point i.e the point where numbers larger will be evaluated with an L1 loss while number smaller will be evaluated with an L2 loss. | ## Example ```php use Rubix\ML\NeuralNet\CostFunctions\HuberLoss; $costFunction = new HuberLoss(0.5); ```