Class: ClassificationPartitionedModel
Classification loss for observations not used for training
L = kfoldLoss(obj)
L = kfoldLoss(obj,Name,Value)
returns
loss obtained by crossvalidated classification model L
= kfoldLoss(obj
)obj
.
For every fold, this method computes classification loss for infold
observations using a model trained on outoffold observations.
calculates
loss with additional options specified by one or more L
= kfoldLoss(obj
,Name,Value
)Name,Value
pair
arguments. You can specify several namevalue pair arguments in any
order as Name1,Value1,…,NameN,ValueN
.

Object of class 
Specify optional commaseparated pairs of Name,Value
arguments.
Name
is the argument
name and Value
is the corresponding
value. Name
must appear
inside single quotes (' '
).
You can specify several name and value pair
arguments in any order as Name1,Value1,...,NameN,ValueN
.

Indices of folds ranging from Default: 

Function handle or string representing a loss function. Builtin loss functions:
You can write your own loss function in the syntax described in Loss Functions. Default: 

A string for determining the output of
Default: 

Loss, by default the fraction of misclassified data. 
The default classification error is the fraction of the data X
that obj
misclassifies,
where Y
are the true classifications.
Weighted classification error is the sum of weight i times
the Boolean value that is 1
when obj
misclassifies
the ith row of X
, divided by
the sum of the weights.
The builtin loss functions are:
'binodeviance'
— For binary
classification, assume the classes y_{n} are 1
and 1
.
With weight vector w normalized to have sum 1
,
and predictions of row n of data X as f(X_{n}),
the binomial deviance is
$$\sum {w}_{n}\mathrm{log}\left(1+\mathrm{exp}\left(2{y}_{n}f\left({X}_{n}\right)\right)\right)}.$$
'exponential'
— With the
same definitions as for 'binodeviance'
, the exponential
loss is
$$\sum {w}_{n}\mathrm{exp}\left({y}_{n}f\left({X}_{n}\right)\right)}.$$
'classiferror'
— Predict
the label with the largest posterior probability. The loss is then
the fraction of misclassified observations.
'hinge'
— Classification
error measure that has the form
$$L=\frac{{\displaystyle \sum}_{j=1}^{n}{w}_{j}\mathrm{max}\left\{0,1{y}_{j}\prime f\left({X}_{j}\right)\right\}}{{\displaystyle \sum}_{j=1}^{n}{w}_{j}},$$
where:
w_{j} is weight j.
For binary classification, y_{j} = 1 for the positive class and 1 for the negative class. For problems where the number of classes K > 3, y_{j} is a vector of 0s, but with a 1 in the position corresponding to the true class, e.g., if the second observation is in the third class and K = 4, then y_{2} = [0 0 1 0]′.
$$f({X}_{j})$$ is, for binary classification, the posterior probability or, for K > 3, a vector of posterior probabilities for each class given observation j.
'mincost'
— Predict the
label with the smallest expected misclassification cost, with expectation
taken over the posterior probability, and cost as given by the Cost
property
of the classifier (a matrix). The loss is then the true misclassification
cost averaged over the observations.
To write your own loss function, create a function file in this form:
function loss = lossfun(C,S,W,COST)
N
is the number of rows of X
.
K
is the number of classes in the
classifier, represented in the ClassNames
property.
C
is an N
byK
logical
matrix, with one true
per row for the true class.
The index for each class is its position in the ClassNames
property.
S
is an N
byK
numeric
matrix. S
is a matrix of posterior probabilities
for classes with one row per observation, similar to the posterior
output
from predict
.
W
is a numeric vector with N
elements,
the observation weights. If you pass W
, the elements
are normalized to sum to the prior probabilities in the respective
classes.
COST
is a K
byK
numeric
matrix of misclassification costs. For example, you can use COST = ones(K)  eye(K)
,
which means a cost of 0
for correct classification,
and 1
for misclassification.
The output loss
should be a scalar.
Pass the function handle @
as
the value of the lossfun
LossFun
namevalue pair.
Find the average crossvalidated classification error for a
model of the ionosphere
data:
load ionosphere tree = fitctree(X,Y); cvtree = crossval(tree); L = kfoldLoss(cvtree) L = 0.1197
ClassificationPartitionedModel
 crossval
 kfoldEdge
 kfoldfun
 kfoldMargin
 kfoldPredict