Classification loss functions measure the predictive
inaccuracy of classification models. When you compare the same type of loss among many
models, a lower loss indicates a better predictive model.
Consider the following scenario.
L is the weighted average classification loss.
n is the sample size.
For binary classification:
y_{j} is the observed class
label. The software codes it as –1 or 1, indicating the negative or
positive class, respectively.
f(X_{j})
is the raw classification score for observation (row)
j of the predictor data
X.
m_{j} =
y_{j}f(X_{j})
is the classification score for classifying observation
j into the class corresponding to
y_{j}. Positive
values of m_{j} indicate
correct classification and do not contribute much to the average
loss. Negative values of
m_{j} indicate incorrect
classification and contribute significantly to the average
loss.
For algorithms that support multiclass classification (that is,
K ≥ 3):
y_{j}^{*}
is a vector of K – 1 zeros, with 1 in the
position corresponding to the true, observed class
y_{j}. For example,
if the true class of the second observation is the third class and
K = 4, then
y^{*}_{2}
= [0 0 1 0]′. The order of the classes corresponds to the order in
the ClassNames
property of the input
model.
f(X_{j})
is the length K vector of class scores for
observation j of the predictor data
X. The order of the scores corresponds to the
order of the classes in the ClassNames
property
of the input model.
m_{j} =
y_{j}^{*}′f(X_{j}).
Therefore, m_{j} is the
scalar classification score that the model predicts for the true,
observed class.
The weight for observation j is
w_{j}. The software normalizes
the observation weights so that they sum to the corresponding prior class
probability. The software also normalizes the prior probabilities so they sum to
1. Therefore,
Given this scenario, the following table describes the supported loss
functions that you can specify by using the 'LossFun'
namevalue pair
argument.
Loss Function  Value of LossFun  Equation 

Binomial deviance  'binodeviance'  $$L={\displaystyle \sum _{j=1}^{n}{w}_{j}\mathrm{log}\left\{1+\mathrm{exp}\left[2{m}_{j}\right]\right\}}.$$ 
Exponential loss  'exponential'  $$L={\displaystyle \sum _{j=1}^{n}{w}_{j}\mathrm{exp}\left({m}_{j}\right)}.$$ 
Classification error  'classiferror'  $$L={\displaystyle \sum _{j=1}^{n}{w}_{j}}I\left\{{\widehat{y}}_{j}\ne {y}_{j}\right\}.$$ It is the weighted fraction of
misclassified observations where $${\widehat{y}}_{j}$$ is the class label corresponding to the class with the
maximal posterior probability.
I{x} is the indicator
function. 
Hinge loss  'hinge'  $$L={\displaystyle \sum}_{j=1}^{n}{w}_{j}\mathrm{max}\left\{0,1{m}_{j}\right\}.$$ 
Logit loss  'logit'  $$L={\displaystyle \sum _{j=1}^{n}{w}_{j}\mathrm{log}\left(1+\mathrm{exp}\left({m}_{j}\right)\right)}.$$ 
Minimal cost  'mincost'  Minimal cost. The software computes the weighted minimal cost using
this procedure for observations j =
1,...,n.
Estimate the 1byK vector of expected
classification costs for observation j:
f(X_{j})
is the column vector of class posterior probabilities for
binary and multiclass classification. C
is the cost matrix that the input model stores in the
Cost property. For observation j, predict the class
label corresponding to the minimum expected classification
cost:
Using C, identify the cost incurred
(c_{j}) for
making the prediction.
The weighted, average, minimum cost loss is

Quadratic loss  'quadratic'  $$L={\displaystyle \sum _{j=1}^{n}{w}_{j}{\left(1{m}_{j}\right)}^{2}}.$$ 
This figure compares the loss functions (except 'mincost'
) for one
observation over m. Some functions are normalized to pass through [0,1].