# kfoldLoss

Classification loss for observations not used in training

## Description

returns
the cross-validated classification
error rates estimated by the cross-validated, error-correcting
output codes (ECOC) model composed of linear classification models `L`

= kfoldLoss(`CVMdl`

)`CVMdl`

.
That is, for every fold, `kfoldLoss`

estimates the
classification error rate for observations that it holds out when
it trains using all other observations. `kfoldLoss`

applies
the same data used create `CVMdl`

(see `fitcecoc`

).

`L`

contains a classification loss for each
regularization strength in the linear classification models that compose `CVMdl`

.

uses
additional options specified by one or more `L`

= kfoldLoss(`CVMdl`

,`Name,Value`

)`Name,Value`

pair
arguments. For example, specify a decoding scheme, which folds to
use for the loss calculation, or verbosity level.

## Input Arguments

`CVMdl`

— Cross-validated, ECOC model composed of linear classification models

`ClassificationPartitionedLinearECOC`

model
object

Cross-validated, ECOC model composed of linear classification
models, specified as a `ClassificationPartitionedLinearECOC`

model
object. You can create a `ClassificationPartitionedLinearECOC`

model
using `fitcecoc`

and by:

Specifying any one of the cross-validation, name-value pair arguments, for example,

`CrossVal`

Setting the name-value pair argument

`Learners`

to`'linear'`

or a linear classification model template returned by`templateLinear`

To obtain estimates, kfoldLoss applies the same data used
to cross-validate the ECOC model (`X`

and `Y`

).

### Name-Value 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`

.

`BinaryLoss`

— Binary learner loss function

`'hamming'`

| `'linear'`

| `'logit'`

| `'exponential'`

| `'binodeviance'`

| `'hinge'`

| `'quadratic'`

| function handle

Binary learner loss function, specified as the comma-separated
pair consisting of `'BinaryLoss'`

and a built-in,
loss-function name or function handle.

This table contains names and descriptions of the built-in functions, where

*y*is a class label for a particular binary learner (in the set {-1,1,0}),_{j}*s*is the score for observation_{j}*j*, and*g*(*y*,_{j}*s*) is the binary loss formula._{j}Value Description Score Domain *g*(*y*,_{j}*s*)_{j}`'binodeviance'`

Binomial deviance (–∞,∞) log[1 + exp(–2 *y*)]/[2log(2)]_{j}s_{j}`'exponential'`

Exponential (–∞,∞) exp(– *y*)/2_{j}s_{j}`'hamming'`

Hamming [0,1] or (–∞,∞) [1 – sign( *y*)]/2_{j}s_{j}`'hinge'`

Hinge (–∞,∞) max(0,1 – *y*)/2_{j}s_{j}`'linear'`

Linear (–∞,∞) (1 – *y*)/2_{j}s_{j}`'logit'`

Logistic (–∞,∞) log[1 + exp(– *y*)]/[2log(2)]_{j}s_{j}`'quadratic'`

Quadratic [0,1] [1 – *y*(2_{j}*s*– 1)]_{j}^{2}/2The software normalizes the binary losses such that the loss is 0.5 when

*y*= 0. Also, the software calculates the mean binary loss for each class._{j}For a custom binary loss function, e.g.,

`customFunction`

, specify its function handle`'BinaryLoss',@customFunction`

.`customFunction`

should have this formwhere:bLoss = customFunction(M,s)

`M`

is the*K*-by-*L*coding matrix stored in`Mdl.CodingMatrix`

.`s`

is the 1-by-*L*row vector of classification scores.`bLoss`

is the classification loss. This scalar aggregates the binary losses for every learner in a particular class. For example, you can use the mean binary loss to aggregate the loss over the learners for each class.*K*is the number of classes.*L*is the number of binary learners.

For an example of passing a custom binary loss function, see Predict Test-Sample Labels of ECOC Model Using Custom Binary Loss Function.

By default, if all binary learners are linear classification models using:

SVM, then

`BinaryLoss`

is`'hinge'`

Logistic regression, then

`BinaryLoss`

is`'quadratic'`

**Example: **`'BinaryLoss','binodeviance'`

**Data Types: **`char`

| `string`

| `function_handle`

`Decoding`

— Decoding scheme

`'lossweighted'`

(default) | `'lossbased'`

Decoding scheme that aggregates the binary losses, specified as the comma-separated pair
consisting of `'Decoding'`

and `'lossweighted'`

or
`'lossbased'`

. For more information, see Binary Loss.

**Example: **`'Decoding','lossbased'`

`Folds`

— Fold indices to use for classification-score prediction

`1:CVMdl.KFold`

(default) | numeric vector of positive integers

Fold indices to use for classification-score prediction, specified
as the comma-separated pair consisting of `'Folds'`

and
a numeric vector of positive integers. The elements of `Folds`

must
range from `1`

through `CVMdl.KFold`

.

**Example: **`'Folds',[1 4 10]`

**Data Types: **`single`

| `double`

`LossFun`

— Loss function

`'classiferror'`

(default) | function handle

Loss function, specified as the comma-separated pair consisting
of `'LossFun'`

and a function handle or `'classiferror'`

.

You can:

Specify the built-in function

`'classiferror'`

, then the loss function is the classification error.Specify your own function using function handle notation.

For what follows,

`n`

is the number of observations in the training data (`CVMdl.NumObservations`

) and`K`

is the number of classes (`numel(CVMdl.ClassNames)`

). Your function needs the signature`lossvalue =`

, where:(C,S,W,Cost)`lossfun`

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`CVMdl.ClassNames`

.Construct

`C`

by setting`C(p,q) = 1`

if observation`p`

is in class`q`

, for each row. Set every element of row`p`

to`0`

.`S`

is an`n`

-by-`K`

numeric matrix of negated loss values for classes. Each row corresponds to an observation. The column order corresponds to the class order in`CVMdl.ClassNames`

.`S`

resembles the output argument`NegLoss`

of`kfoldPredict`

.`W`

is an`n`

-by-1 numeric vector of observation weights. If you pass`W`

, the software normalizes its elements 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.

Specify your function using

`'LossFun',@lossfun`

.

**Data Types: **`function_handle`

| `char`

| `string`

`Mode`

— Loss aggregation level

`'average'`

(default) | `'individual'`

Loss aggregation level, specified as the comma-separated pair
consisting of `'Mode'`

and `'average'`

or `'individual'`

.

Value | Description |
---|---|

`'average'` | Returns losses averaged over all folds |

`'individual'` | Returns losses for each fold |

**Example: **`'Mode','individual'`

`Options`

— Estimation options

`[]`

(default) | structure array returned by `statset`

Estimation options, specified as the comma-separated pair consisting
of `'Options'`

and a structure array returned by `statset`

.

To invoke parallel computing:

You need a Parallel Computing Toolbox™ license.

Specify

`'Options',statset('UseParallel',true)`

.

`Verbose`

— Verbosity level

`0`

(default) | `1`

Verbosity level, specified as the comma-separated pair consisting of
`'Verbose'`

and `0`

or `1`

.
`Verbose`

controls the number of diagnostic messages that the
software displays in the Command Window.

If `Verbose`

is `0`

, then the software does not display
diagnostic messages. Otherwise, the software displays diagnostic messages.

**Example: **`'Verbose',1`

**Data Types: **`single`

| `double`

## Output Arguments

`L`

— Cross-validated classification losses

numeric scalar | numeric vector | numeric matrix

Cross-validated classification losses, returned
as a numeric scalar, vector, or matrix. The interpretation of `L`

depends
on `LossFun`

.

Let * R* be the number of regularizations
strengths is the cross-validated models (

`CVMdl.Trained{1}.BinaryLearners{1}.Lambda`

)
and *be the number of folds (stored in*

`F`

`CVMdl.KFold`

).If

`Mode`

is`'average'`

, then`L`

is a 1-by-vector.`R`

`L(`

is the average classification loss over all folds of the cross-validated model that uses regularization strength)`j`

.`j`

Otherwise,

`L`

is a-by-`F`

matrix.`R`

`L(`

is the classification loss for fold,`i`

)`j`

of the cross-validated model that uses regularization strength`i`

.`j`

## Examples

### Estimate *k*-Fold Cross-Validation Classification Error

Load the NLP data set.

`load nlpdata`

`X`

is a sparse matrix of predictor data, and `Y`

is a categorical vector of class labels.

Cross-validate an ECOC model of linear classification models.

rng(1); % For reproducibility CVMdl = fitcecoc(X,Y,'Learner','linear','CrossVal','on');

`CVMdl`

is a `ClassificationPartitionedLinearECOC`

model. By default, the software implements 10-fold cross validation.

Estimate the average of the out-of-fold classification error rates.

ce = kfoldLoss(CVMdl)

ce = 0.0958

Alternatively, you can obtain the per-fold classification error rates by specifying the name-value pair `'Mode','individual'`

in `kfoldLoss`

.

### Specify Custom Classification Loss

Load the NLP data set. Transpose the predictor data.

```
load nlpdata
X = X';
```

For simplicity, use the label 'others' for all observations in `Y`

that are not `'simulink'`

, `'dsp'`

, or `'comm'`

.

Y(~(ismember(Y,{'simulink','dsp','comm'}))) = 'others';

Create a linear classification model template that specifies optimizing the objective function using SpaRSA.

t = templateLinear('Solver','sparsa');

Cross-validate an ECOC model of linear classification models using 5-fold cross-validation. Optimize the objective function using SpaRSA. Specify that the predictor observations correspond to columns.

rng(1); % For reproducibility CVMdl = fitcecoc(X,Y,'Learners',t,'KFold',5,'ObservationsIn','columns'); CMdl1 = CVMdl.Trained{1}

CMdl1 = CompactClassificationECOC ResponseName: 'Y' ClassNames: [comm dsp simulink others] ScoreTransform: 'none' BinaryLearners: {6x1 cell} CodingMatrix: [4x6 double] Properties, Methods

`CVMdl`

is a `ClassificationPartitionedLinearECOC`

model. It contains the property `Trained`

, which is a 5-by-1 cell array holding a `CompactClassificationECOC`

models that the software trained using the training set of each fold.

Create a function that takes the minimal loss for each observation, and then averages the minimal losses across all observations. Because the function does not use the class-identifier matrix (`C`

), observation weights (`W`

), and classification cost (`Cost`

), use `~`

to have `kfoldLoss`

ignore its their positions.

lossfun = @(~,S,~,~)mean(min(-S,[],2));

Estimate the average cross-validated classification loss using the minimal loss per observation function. Also, obtain the loss for each fold.

`ce = kfoldLoss(CVMdl,'LossFun',lossfun)`

ce = 0.0243

ceFold = kfoldLoss(CVMdl,'LossFun',lossfun,'Mode','individual')

`ceFold = `*5×1*
0.0244
0.0255
0.0248
0.0240
0.0226

### Find Good Lasso Penalty Using Cross-Validation

To determine a good lasso-penalty strength for an ECOC model composed of linear classification models that use logistic regression learners, implement 5-fold cross-validation.

Load the NLP data set.

`load nlpdata`

`X`

is a sparse matrix of predictor data, and `Y`

is a categorical vector of class labels.

For simplicity, use the label 'others' for all observations in `Y`

that are not `'simulink'`

, `'dsp'`

, or `'comm'`

.

Y(~(ismember(Y,{'simulink','dsp','comm'}))) = 'others';

Create a set of 11 logarithmically-spaced regularization strengths from $$1{0}^{-7}$$ through $$1{0}^{-2}$$.

Lambda = logspace(-7,-2,11);

Create a linear classification model template that specifies to use logistic regression learners, use lasso penalties with strengths in `Lambda`

, train using SpaRSA, and lower the tolerance on the gradient of the objective function to `1e-8`

.

t = templateLinear('Learner','logistic','Solver','sparsa',... 'Regularization','lasso','Lambda',Lambda,'GradientTolerance',1e-8);

Cross-validate the models. To increase execution speed, transpose the predictor data and specify that the observations are in columns.

X = X'; rng(10); % For reproducibility CVMdl = fitcecoc(X,Y,'Learners',t,'ObservationsIn','columns','KFold',5);

`CVMdl`

is a `ClassificationPartitionedLinearECOC`

model.

Dissect `CVMdl`

, and each model within it.

numECOCModels = numel(CVMdl.Trained)

numECOCModels = 5

ECOCMdl1 = CVMdl.Trained{1}

ECOCMdl1 = CompactClassificationECOC ResponseName: 'Y' ClassNames: [comm dsp simulink others] ScoreTransform: 'none' BinaryLearners: {6×1 cell} CodingMatrix: [4×6 double] Properties, Methods

numCLModels = numel(ECOCMdl1.BinaryLearners)

numCLModels = 6

CLMdl1 = ECOCMdl1.BinaryLearners{1}

CLMdl1 = ClassificationLinear ResponseName: 'Y' ClassNames: [-1 1] ScoreTransform: 'logit' Beta: [34023×11 double] Bias: [-0.3169 -0.3169 -0.3168 -0.3168 -0.3168 -0.3167 -0.1725 -0.0805 -0.1762 -0.3450 -0.5174] Lambda: [1.0000e-07 3.1623e-07 1.0000e-06 3.1623e-06 1.0000e-05 3.1623e-05 1.0000e-04 3.1623e-04 1.0000e-03 0.0032 0.0100] Learner: 'logistic' Properties, Methods

Because `fitcecoc`

implements 5-fold cross-validation, `CVMdl`

contains a 5-by-1 cell array of `CompactClassificationECOC`

models that the software trains on each fold. The `BinaryLearners`

property of each `CompactClassificationECOC`

model contains the `ClassificationLinear`

models. The number of `ClassificationLinear`

models within each compact ECOC model depends on the number of distinct labels and coding design. Because `Lambda`

is a sequence of regularization strengths, you can think of `CLMdl1`

as 11 models, one for each regularization strength in `Lambda`

.

Determine how well the models generalize by plotting the averages of the 5-fold classification error for each regularization strength. Identify the regularization strength that minimizes the generalization error over the grid.

ce = kfoldLoss(CVMdl); figure; plot(log10(Lambda),log10(ce)) [~,minCEIdx] = min(ce); minLambda = Lambda(minCEIdx); hold on plot(log10(minLambda),log10(ce(minCEIdx)),'ro'); ylabel('log_{10} 5-fold classification error') xlabel('log_{10} Lambda') legend('MSE','Min classification error') hold off

Train an ECOC model composed of linear classification model using the entire data set, and specify the minimal regularization strength.

t = templateLinear('Learner','logistic','Solver','sparsa',... 'Regularization','lasso','Lambda',minLambda,'GradientTolerance',1e-8); MdlFinal = fitcecoc(X,Y,'Learners',t,'ObservationsIn','columns');

To estimate labels for new observations, pass `MdlFinal`

and the new data to `predict`

.

## More About

### Binary Loss

A *binary loss* is a function
of the class and classification score that determines how well a binary
learner classifies an observation into the class.

Suppose the following:

*m*is element (_{kj}*k*,*j*) of the coding design matrix*M*(that is, the code corresponding to class*k*of binary learner*j*).*s*is the score of binary learner_{j}*j*for an observation.*g*is the binary loss function.$$\widehat{k}$$ is the predicted class for the observation.

In *loss-based decoding*
[Escalera et al.], the class producing the minimum sum of the binary losses over
binary learners determines the predicted class of an observation, that is,

$$\widehat{k}=\underset{k}{\text{argmin}}{\displaystyle \sum _{j=1}^{L}\left|{m}_{kj}\right|g}({m}_{kj},{s}_{j}).$$

In *loss-weighted decoding*
[Escalera et al.], the class producing the minimum average of the binary losses
over binary learners determines the predicted class of an observation, that is,

$$\widehat{k}=\underset{k}{\text{argmin}}\frac{{\displaystyle \sum _{j=1}^{L}\left|{m}_{kj}\right|g}({m}_{kj},{s}_{j})}{{\displaystyle \sum}_{j=1}^{L}\left|{m}_{kj}\right|}.$$

Allwein et al. suggest that loss-weighted decoding improves classification accuracy by keeping loss values for all classes in the same dynamic range.

This table summarizes the supported loss functions, where
*y _{j}* is a class label for a particular binary
learner (in the set {–1,1,0}),

*s*is the score for observation

_{j}*j*, and

*g*(

*y*,

_{j}*s*).

_{j}Value | Description | Score Domain | g(y,_{j}s)_{j} |
---|---|---|---|

`'binodeviance'` | Binomial deviance | (–∞,∞) | log[1 +
exp(–2y)]/[2log(2)]_{j}s_{j} |

`'exponential'` | Exponential | (–∞,∞) | exp(–y)/2_{j}s_{j} |

`'hamming'` | Hamming | [0,1] or (–∞,∞) | [1 – sign(y)]/2_{j}s_{j} |

`'hinge'` | Hinge | (–∞,∞) | max(0,1 – y)/2_{j}s_{j} |

`'linear'` | Linear | (–∞,∞) | (1 – y)/2_{j}s_{j} |

`'logit'` | Logistic | (–∞,∞) | log[1 +
exp(–y)]/[2log(2)]_{j}s_{j} |

`'quadratic'` | Quadratic | [0,1] | [1 – y(2_{j}s –
1)]_{j}^{2}/2 |

The software normalizes binary losses such that the loss is 0.5 when
*y _{j}* = 0, and aggregates using the average
of the binary learners [Allwein et al.].

Do not confuse the binary loss with the overall classification loss (specified by the
`'LossFun'`

name-value pair argument of the `loss`

and
`predict`

object functions), which measures how well an ECOC classifier
performs as a whole.

### Classification Error

The *classification error* is
a binary classification error measure that has the form

$$L=\frac{{\displaystyle \sum _{j=1}^{n}{w}_{j}{e}_{j}}}{{\displaystyle \sum _{j=1}^{n}{w}_{j}}},$$

where:

*w*is the weight for observation_{j}*j*. The software renormalizes the weights to sum to 1.*e*= 1 if the predicted class of observation_{j}*j*differs from its true class, and 0 otherwise.

In other words, the classification error is the proportion of observations misclassified by the classifier.

## References

[1] Allwein, E., R. Schapire, and Y. Singer. “Reducing
multiclass to binary: A unifying approach for margin classiﬁers.” *Journal
of Machine Learning Research*. Vol. 1, 2000, pp. 113–141.

[2] Escalera, S., O. Pujol, and P. Radeva.
“On the decoding process in ternary error-correcting output
codes.” *IEEE Transactions on Pattern Analysis and
Machine Intelligence*. Vol. 32, Issue 7, 2010, pp. 120–134.

[3] Escalera, S., O. Pujol, and P. Radeva.
“Separability of ternary codes for sparse designs of error-correcting
output codes.” *Pattern Recogn*. Vol.
30, Issue 3, 2009, pp. 285–297.

## Extended Capabilities

### Automatic Parallel Support

Accelerate code by automatically running computation in parallel using Parallel Computing Toolbox™.

To run in parallel, specify the `'Options'`

name-value argument in the call
to this function and set the `'UseParallel'`

field of the options
structure to `true`

using `statset`

.

For example: `'Options',statset('UseParallel',true)`

For more information about parallel computing, see Run MATLAB Functions with Automatic Parallel Support (Parallel Computing Toolbox).

## See Also

`ClassificationPartitionedLinearECOC`

| `ClassificationECOC`

| `ClassificationLinear`

| `loss`

| `kfoldPredict`

| `fitcecoc`

| `statset`

**Introduced in R2016a**

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