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The logistic loss function is defined as
:<math>V(f(\vec{x}),y) = \frac{1}{ln(2)}ln(1+e^{-yf(\vec{x})})</math>
This function displays a similar convergence rate to the hinge loss function, and since it is continuous, gradient descent methods can be utilized. However, the logistic loss function does not assign zero penalty to any points. Instead, functions which correctly classify points with high confidence, that is high values of <math>|f(\vec{x})|</math>, are penalized less. However, this leads the logistic loss function to be very sensitive to outliers in the data.
== References ==
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