# loss

**Class: **ClassificationLinear

Classification loss for linear classification models

## Description

returns the classification losses for the predictor data in
`L`

= loss(`Mdl`

,`Tbl`

,`ResponseVarName`

)`Tbl`

and the true class labels in
`Tbl.ResponseVarName`

.

specifies options using one or more name-value pair arguments in addition to any
of the input argument combinations in previous syntaxes. For example, you can
specify that columns in the predictor data correspond to observations or specify
the classification loss function.`L`

= loss(___,`Name,Value`

)

## Input Arguments

`Mdl`

— Binary, linear classification model

`ClassificationLinear`

model object

Binary, linear classification model, specified as a `ClassificationLinear`

model object.
You can create a `ClassificationLinear`

model object
using `fitclinear`

.

`X`

— Predictor data

full matrix | sparse matrix

Predictor data, specified as an *n*-by-*p* full or sparse matrix. This orientation of `X`

indicates that rows correspond to individual observations, and columns correspond to individual predictor variables.

**Note**

If you orient your predictor matrix so that observations correspond to columns and specify `'ObservationsIn','columns'`

, then you might experience a significant reduction in computation time.

The length of `Y`

and the number of observations
in `X`

must be equal.

**Data Types: **`single`

| `double`

`Y`

— Class labels

categorical array | character array | string array | logical vector | numeric vector | cell array of character vectors

Class labels, specified as a categorical, character, or string array; logical or numeric vector; or cell array of character vectors.

The data type of

`Y`

must be the same as the data type of`Mdl.ClassNames`

. (The software treats string arrays as cell arrays of character vectors.)The distinct classes in

`Y`

must be a subset of`Mdl.ClassNames`

.If

`Y`

is a character array, then each element must correspond to one row of the array.The length of

`Y`

must be equal to the number of observations in`X`

or`Tbl`

.

**Data Types: **`categorical`

| `char`

| `string`

| `logical`

| `single`

| `double`

| `cell`

`Tbl`

— Sample data

table

Sample data used to train the model, specified as a table. Each row of
`Tbl`

corresponds to one observation, and each column corresponds
to one predictor variable. Optionally, `Tbl`

can contain additional
columns for the response variable and observation weights. `Tbl`

must
contain all the predictors used to train `Mdl`

. Multicolumn variables
and cell arrays other than cell arrays of character vectors are not allowed.

If `Tbl`

contains the response variable used to train `Mdl`

, then you do not need to specify `ResponseVarName`

or `Y`

.

If you train `Mdl`

using sample data contained in a table, then the input
data for `loss`

must also be in a table.

`ResponseVarName`

— Response variable name

name of variable in `Tbl`

Response variable name, specified as the name of a variable in `Tbl`

. If `Tbl`

contains the response variable used to train `Mdl`

, then you do not need to specify `ResponseVarName`

.

If you specify `ResponseVarName`

, then you must specify it as a character
vector or string scalar. For example, if the response variable is stored as
`Tbl.Y`

, then specify `ResponseVarName`

as
`'Y'`

. Otherwise, the software treats all columns of
`Tbl`

, including `Tbl.Y`

, as predictors.

The response variable must be a categorical, character, or string array; a logical or numeric vector; or a cell array of character vectors. If the response variable is a character array, then each element must correspond to one row of the array.

**Data Types: **`char`

| `string`

### 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`

.

`LossFun`

— Loss function

`'classiferror'`

(default) | `'binodeviance'`

| `'exponential'`

| `'hinge'`

| `'logit'`

| `'mincost'`

| `'quadratic'`

| function handle

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.

Value Description `'binodeviance'`

Binomial deviance `'classiferror'`

Misclassified rate in decimal `'exponential'`

Exponential loss `'hinge'`

Hinge loss `'logit'`

Logistic loss `'mincost'`

Minimal expected misclassification cost (for classification scores that are posterior probabilities) `'quadratic'`

Quadratic loss `'mincost'`

is appropriate for classification scores that are posterior probabilities. For linear classification models, logistic regression learners return posterior probabilities as classification scores by default, but SVM learners do not (see`predict`

).To specify a custom loss function, use function handle notation. The function must have this form:

`lossvalue =`

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

The output argument

`lossvalue`

is a scalar.You specify the function name (

).`lossfun`

`C`

is an`n`

-by-`K`

logical matrix with rows indicating the class to which the corresponding observation belongs.`n`

is the number of observations in`Tbl`

or`X`

, and`K`

is the number of distinct classes (`numel(Mdl.ClassNames)`

. The column order corresponds to the class order in`Mdl.ClassNames`

. Create`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`Mdl.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.`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.

**Example: **`'LossFun',@`

`lossfun`

**Data Types: **`char`

| `string`

| `function_handle`

`ObservationsIn`

— Predictor data observation dimension

`'rows'`

(default) | `'columns'`

Predictor data observation dimension, specified as `'rows'`

or
`'columns'`

.

**Note**

If you orient your predictor matrix so that observations correspond to columns and
specify `'ObservationsIn','columns'`

, then you might experience a
significant reduction in computation time. You cannot specify
`'ObservationsIn','columns'`

for predictor data in a
table.

**Data Types: **`char`

| `string`

`Weights`

— Observation weights

`ones(size(X,1),1)`

(default) | numeric vector | name of variable in `Tbl`

Observation weights, specified as the comma-separated pair consisting
of `'Weights'`

and a numeric vector or the name of a
variable in `Tbl`

.

If you specify

`Weights`

as a numeric vector, then the size of`Weights`

must be equal to the number of observations in`X`

or`Tbl`

.If you specify

`Weights`

as the name of a variable in`Tbl`

, then the name must be a character vector or string scalar. For example, if the weights are stored as`Tbl.W`

, then specify`Weights`

as`'W'`

. Otherwise, the software treats all columns of`Tbl`

, including`Tbl.W`

, as predictors.

If you supply weights, then for each regularization strength, `loss`

computes the weighted classification loss and
normalizes weights to sum up to the value of the prior probability in
the respective class.

**Data Types: **`double`

| `single`

## Output Arguments

`L`

— Classification losses

numeric scalar | numeric row vector

## Examples

### Estimate Test-Sample Classification Loss

Load the NLP data set.

`load nlpdata`

`X`

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

is a categorical vector of class labels. There are more than two classes in the data.

The models should identify whether the word counts in a web page are from the Statistics and Machine Learning Toolbox™ documentation. So, identify the labels that correspond to the Statistics and Machine Learning Toolbox™ documentation web pages.

`Ystats = Y == 'stats';`

Train a binary, linear classification model that can identify whether the word counts in a documentation web page are from the Statistics and Machine Learning Toolbox™ documentation. Specify to hold out 30% of the observations. Optimize the objective function using SpaRSA.

rng(1); % For reproducibility CVMdl = fitclinear(X,Ystats,'Solver','sparsa','Holdout',0.30); CMdl = CVMdl.Trained{1};

`CVMdl`

is a `ClassificationPartitionedLinear`

model. It contains the property `Trained`

, which is a 1-by-1 cell array holding a `ClassificationLinear`

model that the software trained using the training set.

Extract the training and test data from the partition definition.

trainIdx = training(CVMdl.Partition); testIdx = test(CVMdl.Partition);

Estimate the training- and test-sample classification error.

ceTrain = loss(CMdl,X(trainIdx,:),Ystats(trainIdx))

ceTrain = 1.3572e-04

ceTest = loss(CMdl,X(testIdx,:),Ystats(testIdx))

ceTest = 5.2804e-04

Because there is one regularization strength in `CMdl`

, `ceTrain`

and `ceTest`

are numeric scalars.

### Specify Custom Classification Loss

Load the NLP data set. Preprocess the data as in Estimate Test-Sample Classification Loss, and transpose the predictor data.

load nlpdata Ystats = Y == 'stats'; X = X';

Train a binary, linear classification model. Specify to hold out 30% of the observations. Optimize the objective function using SpaRSA. Specify that the predictor observations correspond to columns.

rng(1); % For reproducibility CVMdl = fitclinear(X,Ystats,'Solver','sparsa','Holdout',0.30,... 'ObservationsIn','columns'); CMdl = CVMdl.Trained{1};

`CVMdl`

is a `ClassificationPartitionedLinear`

model. It contains the property `Trained`

, which is a 1-by-1 cell array holding a `ClassificationLinear`

model that the software trained using the training set.

Extract the training and test data from the partition definition.

trainIdx = training(CVMdl.Partition); testIdx = test(CVMdl.Partition);

Create an anonymous function that measures linear loss, that is,

$$L=\frac{\sum _{j}-{w}_{j}{y}_{j}{f}_{j}}{\sum _{j}{w}_{j}}.$$

$${w}_{j}$$ is the weight for observation *j*, $${y}_{j}$$ is response *j* (-1 for the negative class, and 1 otherwise), and $${f}_{j}$$ is the raw classification score of observation *j*. Custom loss functions must be written in a particular form. For rules on writing a custom loss function, see the `LossFun`

name-value pair argument.

linearloss = @(C,S,W,Cost)sum(-W.*sum(S.*C,2))/sum(W);

Estimate the training- and test-sample classification loss using the linear loss function.

ceTrain = loss(CMdl,X(:,trainIdx),Ystats(trainIdx),'LossFun',linearloss,... 'ObservationsIn','columns')

ceTrain = -7.8330

ceTest = loss(CMdl,X(:,testIdx),Ystats(testIdx),'LossFun',linearloss,... 'ObservationsIn','columns')

ceTest = -7.7383

### Find Good Lasso Penalty Using Classification Loss

To determine a good lasso-penalty strength for a linear classification model that uses a logistic regression learner, compare test-sample classification error rates.

Load the NLP data set. Preprocess the data as in Specify Custom Classification Loss.

load nlpdata Ystats = Y == 'stats'; X = X'; rng(10); % For reproducibility Partition = cvpartition(Ystats,'Holdout',0.30); testIdx = test(Partition); XTest = X(:,testIdx); YTest = Ystats(testIdx);

Create a set of 11 logarithmically-spaced regularization strengths from $$1{0}^{-6}$$ through $$1{0}^{-0.5}$$.

Lambda = logspace(-6,-0.5,11);

Train binary, linear classification models that use each of the regularization strengths. Optimize the objective function using SpaRSA. Lower the tolerance on the gradient of the objective function to `1e-8`

.

CVMdl = fitclinear(X,Ystats,'ObservationsIn','columns',... 'CVPartition',Partition,'Learner','logistic','Solver','sparsa',... 'Regularization','lasso','Lambda',Lambda,'GradientTolerance',1e-8)

CVMdl = ClassificationPartitionedLinear CrossValidatedModel: 'Linear' ResponseName: 'Y' NumObservations: 31572 KFold: 1 Partition: [1x1 cvpartition] ClassNames: [0 1] ScoreTransform: 'none' Properties, Methods

Extract the trained linear classification model.

Mdl = CVMdl.Trained{1}

Mdl = ClassificationLinear ResponseName: 'Y' ClassNames: [0 1] ScoreTransform: 'logit' Beta: [34023x11 double] Bias: [-12.0872 -12.0872 -12.0872 -12.0872 -12.0872 ... ] Lambda: [1.0000e-06 3.5481e-06 1.2589e-05 4.4668e-05 ... ] Learner: 'logistic' Properties, Methods

`Mdl`

is a `ClassificationLinear`

model object. Because `Lambda`

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

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

.

Estimate the test-sample classification error.

ce = loss(Mdl,X(:,testIdx),Ystats(testIdx),'ObservationsIn','columns');

Because there are 11 regularization strengths, `ce`

is a 1-by-11 vector of classification error rates.

Higher values of `Lambda`

lead to predictor variable sparsity, which is a good quality of a classifier. For each regularization strength, train a linear classification model using the entire data set and the same options as when you cross-validated the models. Determine the number of nonzero coefficients per model.

Mdl = fitclinear(X,Ystats,'ObservationsIn','columns',... 'Learner','logistic','Solver','sparsa','Regularization','lasso',... 'Lambda',Lambda,'GradientTolerance',1e-8); numNZCoeff = sum(Mdl.Beta~=0);

In the same figure, plot the test-sample error rates and frequency of nonzero coefficients for each regularization strength. Plot all variables on the log scale.

figure; [h,hL1,hL2] = plotyy(log10(Lambda),log10(ce),... log10(Lambda),log10(numNZCoeff + 1)); hL1.Marker = 'o'; hL2.Marker = 'o'; ylabel(h(1),'log_{10} classification error') ylabel(h(2),'log_{10} nonzero-coefficient frequency') xlabel('log_{10} Lambda') title('Test-Sample Statistics') hold off

Choose the index of the regularization strength that balances predictor variable sparsity and low classification error. In this case, a value between $$1{0}^{-4}$$ to $$1{0}^{-1}$$ should suffice.

idxFinal = 7;

Select the model from `Mdl`

with the chosen regularization strength.

MdlFinal = selectModels(Mdl,idxFinal);

`MdlFinal`

is a `ClassificationLinear`

model containing one regularization strength. To estimate labels for new observations, pass `MdlFinal`

and the new data to `predict`

.

## More About

### Classification Loss

*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*is the observed class label. The software codes it as –1 or 1, indicating the negative or positive class (or the first or second class in the_{j}`ClassNames`

property), respectively.*f*(*X*) is the positive-class classification score for observation (row)_{j}*j*of the predictor data*X*.*m*=_{j}*y*_{j}*f*(*X*) is the classification score for classifying observation_{j}*j*into the class corresponding to*y*. Positive values of_{j}*m*indicate correct classification and do not contribute much to the average loss. Negative values of_{j}*m*indicate incorrect classification and contribute significantly to the average loss._{j}

For algorithms that support multiclass classification (that is,

*K*≥ 3):*y*is a vector of_{j}^{*}*K*– 1 zeros, with 1 in the position corresponding to the true, observed class*y*. For example, if the true class of the second observation is the third class and_{j}*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*) is the length_{j}*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*). Therefore,_{j}*m*is the scalar classification score that the model predicts for the true, observed class._{j}

The weight for observation

*j*is*w*. 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,_{j}$$\sum _{j=1}^{n}{w}_{j}}=1.$$

Given this scenario, the following table describes the supported loss
functions that you can specify by using the `'LossFun'`

name-value pair
argument.

Loss Function | Value of `LossFun` | Equation |
---|---|---|

Binomial deviance | `'binodeviance'` | $$L={\displaystyle \sum _{j=1}^{n}{w}_{j}\mathrm{log}\left\{1+\mathrm{exp}\left[-2{m}_{j}\right]\right\}}.$$ |

Misclassified rate in decimal | `'classiferror'` | $$L={\displaystyle \sum _{j=1}^{n}{w}_{j}}I\left\{{\widehat{y}}_{j}\ne {y}_{j}\right\}.$$ $${\widehat{y}}_{j}$$ is the class label corresponding to the class with the
maximal score. |

Cross-entropy loss | `'crossentropy'` |
The weighted cross-entropy loss is $$L=-{\displaystyle \sum _{j=1}^{n}\frac{{\tilde{w}}_{j}\mathrm{log}({m}_{j})}{Kn}},$$ where the weights $${\tilde{w}}_{j}$$ are normalized to sum to |

Exponential loss | `'exponential'` | $$L={\displaystyle \sum _{j=1}^{n}{w}_{j}\mathrm{exp}\left(-{m}_{j}\right)}.$$ |

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 expected misclassification cost | `'mincost'` |
The software computes
the weighted minimal expected classification cost using this procedure
for observations Estimate the expected misclassification cost of classifying the observation *X*into the class_{j}*k*:$${\gamma}_{jk}={\left(f{\left({X}_{j}\right)}^{\prime}C\right)}_{k}.$$ *f*(*X*) is the column vector of class posterior probabilities for binary and multiclass classification for the observation_{j}*X*._{j}*C*is the cost matrix stored in the`Cost` property of the model.For observation *j*, predict the class label corresponding to the minimal expected misclassification cost:$${\widehat{y}}_{j}=\underset{k=1,\mathrm{...},K}{\text{argmin}}{\gamma}_{jk}.$$ Using *C*, identify the cost incurred (*c*) for making the prediction._{j}
The weighted average of the minimal expected misclassification cost loss is $$L={\displaystyle \sum _{j=1}^{n}{w}_{j}{c}_{j}}.$$ If you use the default cost matrix (whose element
value is 0 for correct classification and 1 for incorrect
classification), then the |

Quadratic loss | `'quadratic'` | $$L={\displaystyle \sum _{j=1}^{n}{w}_{j}{\left(1-{m}_{j}\right)}^{2}}.$$ |

This figure compares the loss functions (except `'crossentropy'`

and
`'mincost'`

) over the score *m* for one observation.
Some functions are normalized to pass through the point (0,1).

## Algorithms

By default, observation weights are prior class probabilities.
If you supply weights using `Weights`

, then the
software normalizes them to sum to the prior probabilities in the
respective classes. The software uses the renormalized weights to
estimate the weighted classification loss.

## Extended Capabilities

### Tall Arrays

Calculate with arrays that have more rows than fit in memory.

Usage notes and limitations:

`loss`

does not support tall`table`

data.

For more information, see Tall Arrays.

## See Also

**Introduced in R2016a**

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