# kfoldLoss

Regression loss for observations not used in training

## Description

returns
the cross-validated mean squared error (MSE) obtained by the cross-validated,
linear regression model `L`

= kfoldLoss(`CVMdl`

)`CVMdl`

. That is, for every
fold, `kfoldLoss`

estimates the regression loss for
observations that it holds out when it trains using all other observations.

`L`

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

.

uses
additional options specified by one or more `L`

= kfoldLoss(`CVMdl`

,`Name,Value`

)`Name,Value`

pair
arguments. For example, indicate which folds to use for the loss calculation
or specify the regression-loss function.

## Input Arguments

`CVMdl`

— Cross-validated, linear regression model

`RegressionPartitionedLinear`

model object

Cross-validated, linear regression model, specified as a `RegressionPartitionedLinear`

model object. You can create a
`RegressionPartitionedLinear`

model using `fitrlinear`

and specifying any of the one of the cross-validation,
name-value pair arguments, for example, `CrossVal`

.

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

and `Y`

).

### 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.*

`Folds`

— Fold indices to use for response prediction

`1:CVMdl.KFold`

(default) | numeric vector of positive integers

Fold indices to use for response 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

`'mse'`

(default) | `'epsiloninsensitive'`

| 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. Also, in the table, $$f\left(x\right)=x\beta +b.$$

*β*is a vector of*p*coefficients.*x*is an observation from*p*predictor variables.*b*is the scalar bias.

Value Description `'epsiloninsensitive'`

Epsilon-insensitive loss: $$\ell \left[y,f\left(x\right)\right]=\mathrm{max}\left[0,\left|y-f\left(x\right)\right|-\epsilon \right]$$ `'mse'`

MSE: $$\ell \left[y,f\left(x\right)\right]={\left[y-f\left(x\right)\right]}^{2}$$ `'epsiloninsensitive'`

is appropriate for SVM learners only.Specify your own function using function handle notation.

Let

`n`

be the number of observations in`X`

. Your function must have this signaturewhere:`lossvalue =`

(Y,Yhat,W)`lossfun`

The output argument

`lossvalue`

is a scalar.You choose the function name (

).`lossfun`

`Y`

is an`n`

-dimensional vector of observed responses.`kfoldLoss`

passes the input argument`Y`

in for`Y`

.`Yhat`

is an`n`

-dimensional vector of predicted responses, which is similar to the output of`predict`

.`W`

is an`n`

-by-1 numeric vector of observation weights.

Specify your function using

`'LossFun',@`

.`lossfun`

**Data Types: **`char`

| `string`

| `function_handle`

`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'`

`PredictionForMissingValue`

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

`"median"`

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

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

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

Numeric scalar | `kfoldLoss` 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 `kfoldLoss`

does not use
the observation in the loss computation.

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

**Data Types: **`single`

| `double`

| `char`

| `string`

## Output Arguments

`L`

— Cross-validated regression losses

numeric scalar | numeric vector | numeric matrix

Cross-validated regression 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 (stored in

`numel(CVMdl.Trained{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 regression loss over all folds of the cross-validated model that uses regularization strength)`j`

.`j`

Otherwise,

`L`

is an-by-`F`

matrix.`R`

`L(`

is the regression loss for fold,`i`

)`j`

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

.`j`

To estimate `L`

,
`kfoldLoss`

uses the data that created
`CVMdl`

(see `X`

and `Y`

).

## Examples

### Estimate *k*-Fold Mean Squared Error

Simulate 10000 observations from this model

$$y={x}_{100}+2{x}_{200}+e.$$

$$X=\{{x}_{1},...,{x}_{1000}\}$$ is a 10000-by-1000 sparse matrix with 10% nonzero standard normal elements.

*e*is random normal error with mean 0 and standard deviation 0.3.

```
rng(1) % For reproducibility
n = 1e4;
d = 1e3;
nz = 0.1;
X = sprandn(n,d,nz);
Y = X(:,100) + 2*X(:,200) + 0.3*randn(n,1);
```

Cross-validate a linear regression model using SVM learners.

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

`CVMdl`

is a `RegressionPartitionedLinear`

model. By default, the software implements 10-fold cross validation. You can alter the number of folds using the `'KFold'`

name-value pair argument.

Estimate the average of the test-sample MSEs.

mse = kfoldLoss(CVMdl)

mse = 0.1735

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

in `kfoldLoss`

.

### Specify Custom Regression Loss

Simulate data as in Estimate k-Fold Mean Squared Error.

rng(1) % For reproducibility n = 1e4; d = 1e3; nz = 0.1; X = sprandn(n,d,nz); Y = X(:,100) + 2*X(:,200) + 0.3*randn(n,1); X = X'; % Put observations in columns for faster training

Cross-validate a linear regression model using 10-fold cross-validation. Optimize the objective function using SpaRSA.

CVMdl = fitrlinear(X,Y,'CrossVal','on','ObservationsIn','columns',... 'Solver','sparsa');

`CVMdl`

is a `RegressionPartitionedLinear`

model. It contains the property `Trained`

, which is a 10-by-1 cell array holding `RegressionLinear`

models that the software trained using the training set.

Create an anonymous function that measures Huber loss ($$\delta $$ = 1), that is,

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

where

$$\begin{array}{l}\\ {\ell}_{j}=\{\begin{array}{c}0.5{\underset{}{\overset{\u02c6}{{e}_{j}}}}^{2};\\ \left|\underset{}{\overset{\u02c6}{{e}_{j}}}\right|-0.5;\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\end{array}\begin{array}{c}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\left|\underset{}{\overset{\u02c6}{{e}_{j}}}\right|\le 1\\ \phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\left|\underset{}{\overset{\u02c6}{{e}_{j}}}\right|>1\end{array}.\end{array}$$

$\underset{}{\overset{\u02c6}{{e}_{j}}}$ is the residual for 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.

```
huberloss = @(Y,Yhat,W)sum(W.*((0.5*(abs(Y-Yhat)<=1).*(Y-Yhat).^2) + ...
((abs(Y-Yhat)>1).*abs(Y-Yhat)-0.5)))/sum(W);
```

Estimate the average Huber loss over the folds. Also, obtain the Huber loss for each fold.

`mseAve = kfoldLoss(CVMdl,'LossFun',huberloss)`

mseAve = -0.4448

mseFold = kfoldLoss(CVMdl,'LossFun',huberloss,'Mode','individual')

`mseFold = `*10×1*
-0.4454
-0.4473
-0.4453
-0.4469
-0.4434
-0.4434
-0.4465
-0.4430
-0.4438
-0.4426

### Find Good Lasso Penalty Using Cross-Validation

To determine a good lasso-penalty strength for a linear regression model that uses least squares, implement 5-fold cross-validation.

Simulate 10000 observations from this model

$$y={x}_{100}+2{x}_{200}+e.$$

$$X=\{{x}_{1},...,{x}_{1000}\}$$ is a 10000-by-1000 sparse matrix with 10% nonzero standard normal elements.

*e*is random normal error with mean 0 and standard deviation 0.3.

```
rng(1) % For reproducibility
n = 1e4;
d = 1e3;
nz = 0.1;
X = sprandn(n,d,nz);
Y = X(:,100) + 2*X(:,200) + 0.3*randn(n,1);
```

Create a set of 15 logarithmically-spaced regularization strengths from $$1{0}^{-5}$$ through $$1{0}^{-1}$$.

Lambda = logspace(-5,-1,15);

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

X = X'; CVMdl = fitrlinear(X,Y,'ObservationsIn','columns','KFold',5,'Lambda',Lambda,... 'Learner','leastsquares','Solver','sparsa','Regularization','lasso'); numCLModels = numel(CVMdl.Trained)

numCLModels = 5

`CVMdl`

is a `RegressionPartitionedLinear`

model. Because `fitrlinear`

implements 5-fold cross-validation, `CVMdl`

contains 5 `RegressionLinear`

models that the software trains on each fold.

Display the first trained linear regression model.

Mdl1 = CVMdl.Trained{1}

Mdl1 = RegressionLinear ResponseName: 'Y' ResponseTransform: 'none' Beta: [1000x15 double] Bias: [-0.0049 -0.0049 -0.0049 -0.0049 -0.0049 -0.0048 -0.0044 -0.0037 -0.0030 -0.0031 -0.0033 -0.0036 -0.0041 -0.0051 -0.0071] Lambda: [1.0000e-05 1.9307e-05 3.7276e-05 7.1969e-05 1.3895e-04 2.6827e-04 5.1795e-04 1.0000e-03 0.0019 0.0037 0.0072 0.0139 0.0268 0.0518 0.1000] Learner: 'leastsquares'

`Mdl1`

is a `RegressionLinear`

model object. `fitrlinear`

constructed `Mdl1`

by training on the first four folds. Because `Lambda`

is a sequence of regularization strengths, you can think of `Mdl1`

as 15 models, one for each regularization strength in `Lambda`

.

Estimate the cross-validated MSE.

mse = kfoldLoss(CVMdl);

Higher values of `Lambda`

lead to predictor variable sparsity, which is a good quality of a regression model. For each regularization strength, train a linear regression 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 = fitrlinear(X,Y,'ObservationsIn','columns','Lambda',Lambda,... 'Learner','leastsquares','Solver','sparsa','Regularization','lasso'); numNZCoeff = sum(Mdl.Beta~=0);

In the same figure, plot the cross-validated MSE and frequency of nonzero coefficients for each regularization strength. Plot all variables on the log scale.

figure [h,hL1,hL2] = plotyy(log10(Lambda),log10(mse),... log10(Lambda),log10(numNZCoeff)); hL1.Marker = 'o'; hL2.Marker = 'o'; ylabel(h(1),'log_{10} MSE') ylabel(h(2),'log_{10} nonzero-coefficient frequency') xlabel('log_{10} Lambda') hold off

Choose the index of the regularization strength that balances predictor variable sparsity and low MSE (for example, `Lambda(10)`

).

idxFinal = 10;

Extract the model with corresponding to the minimal MSE.

MdlFinal = selectModels(Mdl,idxFinal)

MdlFinal = RegressionLinear ResponseName: 'Y' ResponseTransform: 'none' Beta: [1000x1 double] Bias: -0.0050 Lambda: 0.0037 Learner: 'leastsquares'

idxNZCoeff = find(MdlFinal.Beta~=0)

`idxNZCoeff = `*2×1*
100
200

EstCoeff = Mdl.Beta(idxNZCoeff)

`EstCoeff = `*2×1*
1.0051
1.9965

`MdlFinal`

is a `RegressionLinear`

model with one regularization strength. The nonzero coefficients `EstCoeff`

are close to the coefficients that simulated the data.

## Extended Capabilities

### GPU Arrays

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

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

## Version History

**Introduced in R2016a**

### R2024a: Specify GPU arrays (requires Parallel Computing Toolbox)

`kfoldLoss`

fully supports GPU arrays.

### 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.

## See Also

`RegressionPartitionedLinear`

| `RegressionLinear`

| `kfoldPredict`

| `loss`

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