# kfoldLoss

Classification loss for observations not used for training

## Syntax

L = kfoldLoss(obj)
L = kfoldLoss(obj,Name,Value)

## Description

L = kfoldLoss(obj) returns loss obtained by cross-validated classification model obj. For every fold, this method computes classification loss for in-fold observations using a model trained on out-of-fold observations.

L = kfoldLoss(obj,Name,Value) calculates loss with additional options specified by one or more Name,Value pair arguments. You can specify several name-value pair arguments in any order as Name1,Value1,…,NameN,ValueN.

## Input Arguments

 obj Object of class ClassificationPartitionedModel.

### Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

'folds'

Indices of folds ranging from 1 to obj.KFold. Use only these folds for predictions.

Default: 1:obj.KFold

'lossfun'

Loss function, specified as the comma-separated pair consisting of 'LossFun' and a built-in, loss-function name or function handle.

• The following table lists the available loss functions. Specify one using its corresponding character vector or string scalar.

ValueDescription
'binodeviance'Binomial deviance
'classiferror'Classification error
'exponential'Exponential
'hinge'Hinge
'logit'Logistic
'mincost'Minimal expected misclassification cost (for classification scores that are posterior probabilities)

'mincost' is appropriate for classification scores that are posterior probabilities. All models use posterior probabilities as classification scores by default except SVM models. You can specify to use posterior probabilities as classification scores for SVM models by setting 'FitPosterior',true when you cross-validate the model using fitcsvm.

• Specify your own function using function handle notation.

Suppose that n be the number of observations in X and K be the number of distinct classes (numel(obj.ClassNames), obj is the input model). Your function must have this signature

lossvalue = lossfun(C,S,W,Cost)
where:

• The output argument lossvalue is a scalar.

• You choose the function name (lossfun).

• C is an n-by-K logical matrix with rows indicating which class the corresponding observation belongs. The column order corresponds to the class order in obj.ClassNames.

Construct C by setting C(p,q) = 1 if observation p is in class q, for each row. Set all other elements of row p to 0.

• S is an n-by-K numeric matrix of classification scores. The column order corresponds to the class order in obj.ClassNames. S is a matrix of classification scores, similar to the output of predict.

• W is an n-by-1 numeric vector of observation weights. If you pass W, the software normalizes them to sum to 1.

• Cost is a K-by-K numeric matrix of misclassification costs. For example, Cost = ones(K) - eye(K) specifies a cost of 0 for correct classification, and 1 for misclassification.

For more details on loss functions, see Classification Loss.

Default: 'classiferror'

'mode'

A character vector or string scalar for determining the output of kfoldLoss:

• 'average'L is a scalar, the loss averaged over all folds.

• 'individual'L is a vector of length obj.KFold, where each entry is the loss for a fold.

Default: 'average'

## Output Arguments

 L Loss, by default the fraction of misclassified data. L can be a vector, and can mean different things, depending on the name-value pair settings.

## Examples

expand all

Grow a classification tree.

tree = fitctree(X,Y);

Cross-validate the classification tree using 10-fold cross-validation.

cvtree = crossval(tree);

Estimate the cross-validated classification error.

L = kfoldLoss(cvtree)
L = 0.1083

expand all