add docs for ZeroOneLoss

docs/losses/interface.rst

@@ -184,8 +184,6 @@ that can be queried by functions defined in *LearnBase.jl*.
 
 .. function:: isclipable(loss)
 
-.. function:: islipschitzcont_deriv(loss)
-
 .. function:: ismarginbased(loss)
 
 .. function:: isclasscalibrated(loss)

docs/losses/margin.rst

@@ -8,7 +8,21 @@ that are implemented in this package.
 
    Margin-based Losses (Classification)
 
-   Note: The ZeroOneLoss itself is not margin-based
+ZeroOneLoss
+------------
+
+.. class:: ZeroOneLoss
+
+   The classical classification loss. It penalizes every
+   missclassified observation with a loss of `1` while every
+   correctly classified observation has a loss of `0`.
+   It is not convex nor continuous and thus seldomly used directly.
+   Instead one usually works with some classification-calibrated
+   surrogate loss, such as one of those listed below.
+
+.. math::
+
+   L(a) = \begin{cases} 1 & \quad \text{if } a < 0 \\ 0 & \quad \text{if } a >= 0\\ \end{cases}
 
 PerceptronLoss
 ---------------

src/supervised/margin.jl

@@ -7,6 +7,15 @@
 doc"""
     ZeroOneLoss <: MarginLoss
 
+The classical classification loss. It penalizes every missclassified
+observation with a loss of `1` while every correctly classified
+observation has a loss of `0`.
+It is not convex nor continuous and thus seldomly used directly.
+Instead one usually works with some classification-calibrated
+surrogate loss, such as one of those listed below.
+
+`L(a) = a < 0 ? 1 : 0`
+
               Lossfunction                     Derivative
       ┌────────────┬────────────┐      ┌────────────┬────────────┐
     1 │------------┐            │    1 │                         │