# loss

Regression error for support vector machine regression model

## Syntax

`L = loss(mdl,Tbl,ResponseVarName)`

L = loss(mdl,Tbl,Y)

L = loss(mdl,X,Y)

L = loss(___,Name,Value)

## Description

returns the loss for the predictions of the support vector machine (SVM) regression
model, `L`

= loss(`mdl`

,`Tbl`

,`ResponseVarName`

)`mdl`

, based on the predictor data in the table
`Tbl`

and the true response values in
`Tbl.ResponseVarName`

.

returns the loss for the predictions of the support vector machine (SVM) regression
model, `L`

= loss(`mdl`

,`Tbl`

,`Y`

)`mdl`

, based on the predictor data in the table
`X`

and the true response values in the vector
`Y`

.

returns
the loss for the predictions of the support vector machine (SVM) regression
model, `L`

= loss(`mdl`

,`X`

,`Y`

)`mdl`

, based on the predictor data in `X`

and
the true responses in `Y`

.

returns the loss with additional options specified by one or more name-value arguments,
using any of the previous syntaxes. For example, you can specify the loss function or
observation weights.`L`

= loss(___,`Name,Value`

)

## Input Arguments

`mdl`

— SVM regression model

`RegressionSVM`

model | `CompactRegressionSVM`

model

SVM regression model, specified as a `RegressionSVM`

model or `CompactRegressionSVM`

model
returned by `fitrsvm`

or `compact`

,
respectively.

`Tbl`

— Sample data

table

Sample data, 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 of the predictors used to
train `mdl`

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

If you trained `mdl`

using sample data contained in a
`table`

, then the input data for this method must also
be in a table.

**Data Types: **`table`

`ResponseVarName`

— Response variable name

name of a variable in `Tbl`

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

. The
response variable must be a numeric vector.

You must specify `ResponseVarName`

as a character vector or string scalar.
For example, if the response variable `Y`

is stored as
`Tbl.Y`

, then specify
`ResponseVarName`

as `'Y'`

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

,
including `Y`

, as predictors when training the
model.

**Data Types: **`char`

| `string`

`X`

— Predictor data

numeric matrix

Predictor data, specified as a numeric matrix or table. Each
row of `X`

corresponds to one observation (also
known as an instance or example), and each column corresponds to one
variable (also known as a feature).

If you trained `mdl`

using a matrix of predictor
values, then `X`

must be a numeric matrix with *p* columns. *p* is
the number of predictors used to train `mdl`

.

The length of `Y`

and the number of rows
of `X`

must be equal.

**Data Types: **`single`

| `double`

`Y`

— Observed response values

vector of numeric values

Observed response values, specified as a vector of length *n* containing
numeric values. Each entry in `Y`

is the observed
response based on the predictor data in the corresponding row of `X`

.

**Data Types: **`single`

| `double`

### Name-Value Arguments

Specify optional pairs of arguments as
`Name1=Value1,...,NameN=ValueN`

, where `Name`

is
the argument name and `Value`

is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.

*
Before R2021a, use commas to separate each name and value, and enclose*
`Name`

*in quotes.*

`LossFun`

— Loss function

`'mse'`

(default) | `'epsiloninsensitive'`

| function handle

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

and `'mse'`

, `'epsiloninsensitive'`

,
or a function handle.

The following table lists the available loss functions.

Value Loss Function `'mse'`

Weighted Mean Squared Error `'epsiloninsensitive'`

Epsilon-Insensitive Loss Function Specify your own function using function handle notation.

Your function must have the signature

`lossvalue = lossfun(Y,Yfit,W)`

, where:The output argument

`lossvalue`

is a scalar value.You choose the function name (

*lossfun*).`Y`

is an*n*-by-1 numeric vector of observed response values.`Yfit`

is an*n*-by-1 numeric vector of predicted response values, calculated using the corresponding predictor values in`X`

(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.

Specify your function using

`'LossFun',@`

.*lossfun*

**Example: **`'LossFun','epsiloninsensitive'`

**Data Types: **`char`

| `string`

| `function_handle`

`PredictionForMissingValue`

— Predicted response value to use for observations with missing predictor values

`"median"`

(default) | `"mean"`

| `"omitted"`

| numeric scalar

*Since R2023b*

Predicted response value to use for observations with missing predictor values,
specified as `"median"`

, `"mean"`

,
`"omitted"`

, or a numeric scalar.

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

`"median"` | `loss` uses the median of the observed
response values in the training data as the predicted response value for
observations with missing predictor values. |

`"mean"` | `loss` uses the mean of the observed
response values in the training data as the predicted response value for
observations with missing predictor values. |

`"omitted"` | `loss` excludes observations with missing
predictor values from the loss computation. |

Numeric scalar | `loss` uses this value as the predicted
response value for observations with missing predictor values. |

If an observation is missing an observed response value or an observation weight, then
`loss`

does not use the observation in the loss
computation.

**Example: **`"PredictionForMissingValue","omitted"`

**Data Types: **`single`

| `double`

| `char`

| `string`

`Weights`

— Observation weights

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

(default) | numeric vector

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

and a numeric vector. `Weights`

must
be the same length as the number of rows in `X`

.
The software weighs the observations in each row of `X`

using
the corresponding weight value in `Weights`

.

Weights are normalized to sum to 1.

**Data Types: **`single`

| `double`

## Output Arguments

`L`

— Regression loss

scalar value

Regression loss, returned as a scalar value.

## Examples

**Calculate Test Sample Loss for SVM Regression Model**

Calculate the test set mean squared error (MSE) and epsilon-insensitive error of an SVM regression model.

Load the `carsmall`

sample data. Specify `Horsepower`

and `Weight`

as the predictor variables (`X`

), and `MPG`

as the response variable (`Y`

).

```
load carsmall
X = [Horsepower,Weight];
Y = MPG;
```

Delete rows of `X`

and `Y`

where either array has `NaN`

values.

R = rmmissing([X Y]); X = R(:,1:2); Y = R(:,end);

Reserve 10% of the observations as a holdout sample, and extract the training and test indices.

rng default % For reproducibility N = length(Y); cv = cvpartition(N,'HoldOut',0.10); trainInds = training(cv); testInds = test(cv);

Specify the training and test data sets.

XTrain = X(trainInds,:); YTrain = Y(trainInds); XTest = X(testInds,:); YTest = Y(testInds);

Train a linear SVM regression model and standardize the data.

`mdl = fitrsvm(XTrain,YTrain,'Standardize',true)`

mdl = RegressionSVM ResponseName: 'Y' CategoricalPredictors: [] ResponseTransform: 'none' Alpha: [68x1 double] Bias: 23.0248 KernelParameters: [1x1 struct] Mu: [108.8810 2.9419e+03] Sigma: [44.4943 805.1412] NumObservations: 84 BoxConstraints: [84x1 double] ConvergenceInfo: [1x1 struct] IsSupportVector: [84x1 logical] Solver: 'SMO'

`mdl`

is a `RegressionSVM`

model.

Determine how well the trained model generalizes to new predictor values by estimating the test sample mean squared error and epsilon-insensitive error.

lossMSE = loss(mdl,XTest,YTest)

lossMSE = 32.0268

lossEI = loss(mdl,XTest,YTest,'LossFun','epsiloninsensitive')

lossEI = 3.2919

## More About

### Weighted Mean Squared Error

The weighted mean squared error is calculated as follows:

$$\text{mse}=\frac{{\displaystyle \sum _{j=1}^{n}{w}_{j}{\left(f\left({x}_{j}\right)-{y}_{j}\right)}^{2}}}{{\displaystyle \sum _{j=1}^{n}{w}_{j}}}\text{\hspace{0.17em}},$$

where:

*n*is the number of rows of data*x*is the_{j}*j*th row of data*y*is the true response to_{j}*x*_{j}*f*(*x*) is the response prediction of the SVM regression model_{j}`mdl`

to*x*_{j}*w*is the vector of weights.

The weights in *w* are all equal to one by
default. You can specify different values for weights using the `'Weights'`

name-value
pair argument. If you specify weights, each value is divided by the
sum of all weights, such that the normalized weights add to one.

### Epsilon-Insensitive Loss Function

The epsilon-insensitive loss function ignores errors that are within the distance epsilon (ε) of the function value. It is formally described as:

$$Los{s}_{\epsilon}=\{\begin{array}{c}0\text{\hspace{0.17em}},\text{\hspace{0.17em}}if\text{\hspace{0.17em}}\left|y-f\left(x\right)\right|\le \epsilon \\ \left|y-f\left(x\right)\right|-\epsilon \text{\hspace{0.17em}},\text{\hspace{0.17em}}otherwise.\end{array}$$

The mean epsilon-insensitive loss is calculated as follows:

$$Loss=\frac{{\displaystyle \sum _{j=1}^{n}{w}_{j}\mathrm{max}\left(0,\left|{y}_{j}-f\left({x}_{j}\right)\right|-\epsilon \right)}}{{\displaystyle \sum _{j=1}^{n}{w}_{j}}}\text{\hspace{0.17em}},$$

where:

*n*is the number of rows of data*x*is the_{j}*j*th row of data*y*is the true response to_{j}*x*_{j}*f*(*x*) is the response prediction of the SVM regression model_{j}`mdl`

to*x*_{j}*w*is the vector of weights.

The weights in *w* are all equal to one by
default. You can specify different values for weights using the `'Weights'`

name-value
pair argument. If you specify weights, each value is divided by the
sum of all weights, such that the normalized weights add to one.

## Tips

If

`mdl`

is a cross-validated`RegressionPartitionedSVM`

model, use`kfoldLoss`

instead of`loss`

to calculate the regression error.

## Extended Capabilities

### Tall Arrays

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

This function fully supports tall arrays. For more information, see Tall Arrays.

### GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™. (since R2023a)

This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

## Version History

**Introduced in R2015b**

### R2023b: Specify predicted response value to use for observations with missing predictor values

Starting in R2023b, when you predict or compute the loss, some regression models allow you to specify the predicted response value for observations with missing predictor values. Specify the `PredictionForMissingValue`

name-value argument to use a numeric scalar, the training set median, or the training set mean as the predicted value. When computing the loss, you can also specify to omit observations with missing predictor values.

This table lists the object functions that support the
`PredictionForMissingValue`

name-value argument. By default, the
functions use the training set median as the predicted response value for observations with
missing predictor values.

Model Type | Model Objects | Object Functions |
---|---|---|

Gaussian process regression (GPR) model | `RegressionGP` , `CompactRegressionGP` | `loss` , `predict` , `resubLoss` , `resubPredict` |

`RegressionPartitionedGP` | `kfoldLoss` , `kfoldPredict` | |

Gaussian kernel regression model | `RegressionKernel` | `loss` , `predict` |

`RegressionPartitionedKernel` | `kfoldLoss` , `kfoldPredict` | |

Linear regression model | `RegressionLinear` | `loss` , `predict` |

`RegressionPartitionedLinear` | `kfoldLoss` , `kfoldPredict` | |

Neural network regression model | `RegressionNeuralNetwork` , `CompactRegressionNeuralNetwork` | `loss` , `predict` , `resubLoss` , `resubPredict` |

`RegressionPartitionedNeuralNetwork` | `kfoldLoss` , `kfoldPredict` | |

Support vector machine (SVM) regression model | `RegressionSVM` , `CompactRegressionSVM` | `loss` , `predict` , `resubLoss` , `resubPredict` |

`RegressionPartitionedSVM` | `kfoldLoss` , `kfoldPredict` |

In previous releases, the regression model `loss`

and `predict`

functions listed above used `NaN`

predicted response values for observations with missing predictor values. The software omitted observations with missing predictor values from the resubstitution ("resub") and cross-validation ("kfold") computations for prediction and loss.

### R2023a: GPU array support

Starting in R2023a, `loss`

fully supports GPU arrays.

### R2022a: `loss`

can return NaN for predictor data with missing
values

The `loss`

function no longer omits an observation with a
NaN prediction when computing the weighted average regression loss. Therefore,
`loss`

can now return NaN when the predictor data
`X`

or the predictor variables in `Tbl`

contain any missing values. In most cases, if the test set observations do not contain
missing predictors, the `loss`

function does not return
NaN.

This change improves the automatic selection of a regression model when you use
`fitrauto`

.
Before this change, the software might select a model (expected to best predict the
responses for new data) with few non-NaN predictors.

If `loss`

in your code returns NaN, you can update your code
to avoid this result. Remove or replace the missing values by using `rmmissing`

or `fillmissing`

, respectively.

The following table shows the regression models for which the
`loss`

object function might return NaN. For more details,
see the Compatibility Considerations for each `loss`

function.

Model Type | Full or Compact Model Object | `loss` Object Function |
---|---|---|

Gaussian process regression (GPR) model | `RegressionGP` , `CompactRegressionGP` | `loss` |

Gaussian kernel regression model | `RegressionKernel` | `loss` |

Linear regression model | `RegressionLinear` | `loss` |

Neural network regression model | `RegressionNeuralNetwork` , `CompactRegressionNeuralNetwork` | `loss` |

Support vector machine (SVM) regression model | `RegressionSVM` , `CompactRegressionSVM` | `loss` |

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