# loss

## Description

returns the regression loss for the trained regression neural network
`L`

= loss(`Mdl`

,`Tbl`

,`ResponseVarName`

)`Mdl`

using the predictor data in table `Tbl`

and
the response values in the `ResponseVarName`

table variable.

`L`

is returned as a scalar value that represents the mean squared
error (MSE) by default.

specifies options using one or more name-value 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, specify the loss function, or supply
observation weights.`L`

= loss(___,`Name,Value`

)

## Examples

### Test Set Mean Squared Error of Neural Network

Calculate the test set mean squared error (MSE) of a regression neural network model.

Load the `patients`

data set. Create a table from the data set. Each row corresponds to one patient, and each column corresponds to a diagnostic variable. Use the `Systolic`

variable as the response variable, and the rest of the variables as predictors.

```
load patients
tbl = table(Diastolic,Height,Smoker,Weight,Systolic);
```

Separate the data into a training set `tblTrain`

and a test set `tblTest`

by using a nonstratified holdout partition. The software reserves approximately 30% of the observations for the test data set and uses the rest of the observations for the training data set.

rng("default") % For reproducibility of the partition c = cvpartition(size(tbl,1),"Holdout",0.30); trainingIndices = training(c); testIndices = test(c); tblTrain = tbl(trainingIndices,:); tblTest = tbl(testIndices,:);

Train a regression neural network model using the training set. Specify the `Systolic`

column of `tblTrain`

as the response variable. Specify to standardize the numeric predictors, and set the iteration limit to 50.

Mdl = fitrnet(tblTrain,"Systolic", ... "Standardize",true,"IterationLimit",50);

Calculate the test set MSE. Smaller MSE values indicate better performance.

`testMSE = loss(Mdl,tblTest,"Systolic")`

testMSE = 22.2447

### Select Features to Include in Regression Neural Network

Perform feature selection by comparing test set losses and predictions. Compare the test set metrics for a regression neural network model trained using all the predictors to the test set metrics for a model trained using only a subset of the predictors.

Load the sample file `fisheriris.csv`

, which contains iris data including sepal length, sepal width, petal length, petal width, and species type. Read the file into a table.

`fishertable = readtable('fisheriris.csv');`

Separate the data into a training set `trainTbl`

and a test set `testTbl`

by using a nonstratified holdout partition. The software reserves approximately 30% of the observations for the test data set and uses the rest of the observations for the training data set.

rng("default") c = cvpartition(size(fishertable,1),"Holdout",0.3); trainTbl = fishertable(training(c),:); testTbl = fishertable(test(c),:);

Train one regression neural network model using all the predictors in the training set, and train another model using all the predictors except `PetalWidth`

. For both models, specify `PetalLength`

as the response variable, and standardize the predictors.

allMdl = fitrnet(trainTbl,"PetalLength","Standardize",true); subsetMdl = fitrnet(trainTbl,"PetalLength ~ SepalLength + SepalWidth + Species", ... "Standardize",true);

Compare the test set mean squared error (MSE) of the two models. Smaller MSE values indicate better performance.

allMSE = loss(allMdl,testTbl)

allMSE = 0.0856

subsetMSE = loss(subsetMdl,testTbl)

subsetMSE = 0.0881

For each model, compare the test set predicted petal lengths to the true petal lengths. Plot the predicted petal lengths along the vertical axis and the true petal lengths along the horizontal axis. Points on the reference line indicate correct predictions.

tiledlayout(2,1) % Top axes ax1 = nexttile; allPredictedY = predict(allMdl,testTbl); plot(ax1,testTbl.PetalLength,allPredictedY,".") hold on plot(ax1,testTbl.PetalLength,testTbl.PetalLength) hold off xlabel(ax1,"True Petal Length") ylabel(ax1,"Predicted Petal Length") title(ax1,"All Predictors") % Bottom axes ax2 = nexttile; subsetPredictedY = predict(subsetMdl,testTbl); plot(ax2,testTbl.PetalLength,subsetPredictedY,".") hold on plot(ax2,testTbl.PetalLength,testTbl.PetalLength) hold off xlabel(ax2,"True Petal Length") ylabel(ax2,"Predicted Petal Length") title(ax2,"Subset of Predictors")

Because both models seems to perform well, with predictions scattered near the reference line, consider using the model trained using all predictors except `PetalWidth`

.

## Input Arguments

`Mdl`

— Trained regression neural network

`RegressionNeuralNetwork`

model object | `CompactRegressionNeuralNetwork`

model object

Trained regression neural network, specified as a `RegressionNeuralNetwork`

model object or `CompactRegressionNeuralNetwork`

model object returned by `fitrnet`

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 an additional column for the response variable.
`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

`Tbl`

contains the response variable used to train`Mdl`

, then you do not need to specify`ResponseVarName`

or`Y`

.If you trained

`Mdl`

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

must also be in a table.If you set

`'Standardize',true`

in`fitrnet`

when training`Mdl`

, then the software standardizes the numeric columns of the predictor data using the corresponding means and standard deviations.

**Data Types: **`table`

`ResponseVarName`

— Response variable name

name of variable in `Tbl`

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

. The response variable must be a numeric vector.

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.

**Data Types: **`char`

| `string`

`X`

— Predictor data

numeric matrix

Predictor data, specified as a numeric matrix. By default,
`loss`

assumes that each row of `X`

corresponds to one observation, and each column corresponds to one predictor
variable.

**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.

If you set `'Standardize',true`

in `fitrnet`

when training `Mdl`

, then the software standardizes the numeric
columns of the predictor data using the corresponding means and standard
deviations.

**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.*

**Example: **`loss(Mdl,Tbl,"Response","Weights","W")`

specifies to use the
`Response`

and `W`

variables in the table
`Tbl`

as the response values and observation weights,
respectively.

`LossFun`

— Loss function

`'mse'`

(default) | function handle

Loss function, specified as `'mse'`

or a function handle.

`'mse'`

— Weighted mean squared error.Function handle — To specify a custom loss function, use a function handle. The function must have this form:

lossval =

*lossfun*(Y,YFit,W)The output argument

`lossval`

is a floating-point scalar.You specify the function name (

).`lossfun`

`Y`

is a length*n*numeric vector of observed responses, where*n*is the number of observations in`Tbl`

or`X`

.`YFit`

is a length*n*numeric vector of corresponding predicted responses.`W`

is an*n*-by-1 numeric vector of observation weights.

**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`

`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 all predictor values, 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

nonnegative numeric vector | name of variable in `Tbl`

Observation weights, specified as a nonnegative numeric vector or the name of a
variable in `Tbl`

. The software weights each observation in
`X`

or `Tbl`

with the corresponding value in
`Weights`

. The length of `Weights`

must equal
the number of observations in `X`

or
`Tbl`

.

If you specify the input data as a table `Tbl`

, then
`Weights`

can be the name of a variable in
`Tbl`

that contains a numeric vector. In this case, you must
specify `Weights`

as a character vector or string scalar. For
example, if the weights vector `W`

is stored as
`Tbl.W`

, then specify it as `'W'`

.

By default, `Weights`

is `ones(n,1)`

, where
`n`

is the number of observations in `X`

or
`Tbl`

.

If you supply weights, then `loss`

computes the weighted
regression loss and normalizes weights to sum to 1.

**Data Types: **`single`

| `double`

| `char`

| `string`

## Version History

**Introduced in R2021a**

### 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.

### 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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