# fitrlinear

Fit linear regression model to high-dimensional data

## Syntax

``Mdl = fitrlinear(X,Y)``
``Mdl = fitrlinear(Tbl,ResponseVarName)``
``Mdl = fitrlinear(Tbl,formula)``
``Mdl = fitrlinear(Tbl,Y)``
``Mdl = fitrlinear(X,Y,Name,Value)``
``````[Mdl,FitInfo] = fitrlinear(___)``````
``````[Mdl,FitInfo,HyperparameterOptimizationResults] = fitrlinear(___)``````

## Description

`fitrlinear` efficiently trains linear regression models with high-dimensional, full or sparse predictor data. Available linear regression models include regularized support vector machines (SVM) and least-squares regression methods. `fitrlinear` minimizes the objective function using techniques that reduce computing time (e.g., stochastic gradient descent).

For reduced computation time on a high-dimensional data set that includes many predictor variables, train a linear regression model by using `fitrlinear`. For low- through medium-dimensional predictor data sets, see Alternatives for Lower-Dimensional Data.

example

``Mdl = fitrlinear(X,Y)` returns a trained regression model object `Mdl` that contains the results of fitting a support vector machine regression model to the predictors `X` and response `Y`.`
``Mdl = fitrlinear(Tbl,ResponseVarName)` returns a linear regression model using the predictor variables in the table `Tbl` and the response values in `Tbl.ResponseVarName`.`
``Mdl = fitrlinear(Tbl,formula)` returns a linear regression model using the sample data in the table `Tbl`. The input argument `formula` is an explanatory model of the response and a subset of predictor variables in `Tbl` used to fit `Mdl`.`
``Mdl = fitrlinear(Tbl,Y)` returns a linear regression model using the predictor variables in the table `Tbl` and the response values in vector `Y`.`

example

``Mdl = fitrlinear(X,Y,Name,Value)` 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 to cross-validate, implement least-squares regression, or specify the type of regularization. A good practice is to cross-validate using the `'Kfold'` name-value pair argument. The cross-validation results determine how well the model generalizes.`

example

``````[Mdl,FitInfo] = fitrlinear(___)``` also returns optimization details using any of the previous syntaxes. You cannot request `FitInfo` for cross-validated models.```

example

``````[Mdl,FitInfo,HyperparameterOptimizationResults] = fitrlinear(___)``` also returns hyperparameter optimization details when you pass an `OptimizeHyperparameters` name-value pair.```

## Examples

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Train a linear regression model using SVM, dual SGD, and ridge regularization.

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);```

Train a linear regression model. By default, `fitrlinear` uses support vector machines with a ridge penalty, and optimizes using dual SGD for SVM. Determine how well the optimization algorithm fit the model to the data by extracting a fit summary.

`[Mdl,FitInfo] = fitrlinear(X,Y)`
```Mdl = RegressionLinear ResponseName: 'Y' ResponseTransform: 'none' Beta: [1000x1 double] Bias: -0.0056 Lambda: 1.0000e-04 Learner: 'svm' Properties, Methods ```
```FitInfo = struct with fields: Lambda: 1.0000e-04 Objective: 0.2725 PassLimit: 10 NumPasses: 10 BatchLimit: [] NumIterations: 100000 GradientNorm: NaN GradientTolerance: 0 RelativeChangeInBeta: 0.4907 BetaTolerance: 1.0000e-04 DeltaGradient: 1.5816 DeltaGradientTolerance: 0.1000 TerminationCode: 0 TerminationStatus: {'Iteration limit exceeded.'} Alpha: [10000x1 double] History: [] FitTime: 0.0606 Solver: {'dual'} ```

`Mdl` is a `RegressionLinear` model. You can pass `Mdl` and the training or new data to `loss` to inspect the in-sample mean-squared error. Or, you can pass `Mdl` and new predictor data to `predict` to predict responses for new observations.

`FitInfo` is a structure array containing, among other things, the termination status (`TerminationStatus`) and how long the solver took to fit the model to the data (`FitTime`). It is good practice to use `FitInfo` to determine whether optimization-termination measurements are satisfactory. In this case, `fitrlinear` reached the maximum number of iterations. Because training time is fast, you can retrain the model, but increase the number of passes through the data. Or, try another solver, such as LBFGS.

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

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

• $X=\left\{{x}_{1},...,{x}_{1000}\right\}$ 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 ... ] Lambda: [1.0000e-05 1.9307e-05 3.7276e-05 7.1969e-05 ... ] Learner: 'leastsquares' Properties, Methods ```

`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' Properties, Methods ```
`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.

This example shows how to optimize hyperparameters automatically using `fitrlinear`. The example uses artificial (simulated) data for the model

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

• $X=\left\{{x}_{1},...,{x}_{1000}\right\}$ 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);```

Find hyperparameters that minimize five-fold cross validation loss by using automatic hyperparameter optimization.

For reproducibility, use the `'expected-improvement-plus'` acquisition function.

```hyperopts = struct('AcquisitionFunctionName','expected-improvement-plus'); [Mdl,FitInfo,HyperparameterOptimizationResults] = fitrlinear(X,Y,... 'OptimizeHyperparameters','auto',... 'HyperparameterOptimizationOptions',hyperopts)```
```|=====================================================================================================| | Iter | Eval | Objective: | Objective | BestSoFar | BestSoFar | Lambda | Learner | | | result | log(1+loss) | runtime | (observed) | (estim.) | | | |=====================================================================================================| | 1 | Best | 0.16029 | 0.7907 | 0.16029 | 0.16029 | 2.4206e-09 | svm | | 2 | Best | 0.14496 | 0.63743 | 0.14496 | 0.14601 | 0.001807 | svm | | 3 | Best | 0.13879 | 0.67527 | 0.13879 | 0.14065 | 2.4681e-09 | leastsquares | | 4 | Best | 0.115 | 0.55616 | 0.115 | 0.11501 | 0.021027 | leastsquares | | 5 | Accept | 0.44352 | 0.6156 | 0.115 | 0.1159 | 4.6795 | leastsquares | | 6 | Best | 0.11025 | 0.63043 | 0.11025 | 0.11024 | 0.010671 | leastsquares | | 7 | Accept | 0.13222 | 0.51473 | 0.11025 | 0.11024 | 8.6067e-08 | leastsquares | | 8 | Accept | 0.13262 | 0.58507 | 0.11025 | 0.11023 | 8.5109e-05 | leastsquares | | 9 | Accept | 0.13543 | 0.62007 | 0.11025 | 0.11021 | 2.7562e-06 | leastsquares | | 10 | Accept | 0.15751 | 0.61176 | 0.11025 | 0.11022 | 5.0559e-06 | svm | | 11 | Accept | 0.40673 | 0.68834 | 0.11025 | 0.1102 | 0.52074 | svm | | 12 | Accept | 0.16057 | 0.80978 | 0.11025 | 0.1102 | 0.00014292 | svm | | 13 | Accept | 0.16105 | 0.58199 | 0.11025 | 0.11018 | 1.0079e-07 | svm | | 14 | Accept | 0.12763 | 0.50879 | 0.11025 | 0.11019 | 0.0012085 | leastsquares | | 15 | Accept | 0.13504 | 0.48453 | 0.11025 | 0.11019 | 1.3981e-08 | leastsquares | | 16 | Accept | 0.11041 | 0.58515 | 0.11025 | 0.11026 | 0.0093968 | leastsquares | | 17 | Best | 0.10954 | 0.59682 | 0.10954 | 0.11003 | 0.010393 | leastsquares | | 18 | Accept | 0.10998 | 0.62937 | 0.10954 | 0.11002 | 0.010254 | leastsquares | | 19 | Accept | 0.45314 | 0.50955 | 0.10954 | 0.11001 | 9.9932 | svm | | 20 | Best | 0.10753 | 0.60143 | 0.10753 | 0.10759 | 0.022576 | svm | |=====================================================================================================| | Iter | Eval | Objective: | Objective | BestSoFar | BestSoFar | Lambda | Learner | | | result | log(1+loss) | runtime | (observed) | (estim.) | | | |=====================================================================================================| | 21 | Best | 0.10737 | 0.63705 | 0.10737 | 0.10728 | 0.010171 | svm | | 22 | Accept | 0.13448 | 0.65832 | 0.10737 | 0.10727 | 1.5344e-05 | leastsquares | | 23 | Best | 0.10645 | 0.73373 | 0.10645 | 0.10565 | 0.015495 | svm | | 24 | Accept | 0.13598 | 0.48257 | 0.10645 | 0.10559 | 4.5984e-07 | leastsquares | | 25 | Accept | 0.15962 | 0.71824 | 0.10645 | 0.10556 | 1.4302e-08 | svm | | 26 | Accept | 0.10689 | 0.77732 | 0.10645 | 0.10616 | 0.015391 | svm | | 27 | Accept | 0.13748 | 0.43259 | 0.10645 | 0.10614 | 1.001e-09 | leastsquares | | 28 | Accept | 0.10692 | 0.64502 | 0.10645 | 0.10639 | 0.015761 | svm | | 29 | Accept | 0.10681 | 0.66523 | 0.10645 | 0.10649 | 0.015777 | svm | | 30 | Accept | 0.34314 | 0.58592 | 0.10645 | 0.10651 | 0.39671 | leastsquares | ```

```__________________________________________________________ Optimization completed. MaxObjectiveEvaluations of 30 reached. Total function evaluations: 30 Total elapsed time: 46.3613 seconds Total objective function evaluation time: 18.569 Best observed feasible point: Lambda Learner ________ _______ 0.015495 svm Observed objective function value = 0.10645 Estimated objective function value = 0.10651 Function evaluation time = 0.73373 Best estimated feasible point (according to models): Lambda Learner ________ _______ 0.015777 svm Estimated objective function value = 0.10651 Estimated function evaluation time = 0.65559 ```
```Mdl = RegressionLinear ResponseName: 'Y' ResponseTransform: 'none' Beta: [1000x1 double] Bias: -0.0018 Lambda: 0.0158 Learner: 'svm' Properties, Methods ```
```FitInfo = struct with fields: Lambda: 0.0158 Objective: 0.2309 PassLimit: 10 NumPasses: 10 BatchLimit: [] NumIterations: 99989 GradientNorm: NaN GradientTolerance: 0 RelativeChangeInBeta: 0.0641 BetaTolerance: 1.0000e-04 DeltaGradient: 1.1697 DeltaGradientTolerance: 0.1000 TerminationCode: 0 TerminationStatus: {'Iteration limit exceeded.'} Alpha: [10000x1 double] History: [] FitTime: 0.0681 Solver: {'dual'} ```
```HyperparameterOptimizationResults = BayesianOptimization with properties: ObjectiveFcn: @createObjFcn/inMemoryObjFcn VariableDescriptions: [3x1 optimizableVariable] Options: [1x1 struct] MinObjective: 0.1065 XAtMinObjective: [1x2 table] MinEstimatedObjective: 0.1065 XAtMinEstimatedObjective: [1x2 table] NumObjectiveEvaluations: 30 TotalElapsedTime: 46.3613 NextPoint: [1x2 table] XTrace: [30x2 table] ObjectiveTrace: [30x1 double] ConstraintsTrace: [] UserDataTrace: {30x1 cell} ObjectiveEvaluationTimeTrace: [30x1 double] IterationTimeTrace: [30x1 double] ErrorTrace: [30x1 double] FeasibilityTrace: [30x1 logical] FeasibilityProbabilityTrace: [30x1 double] IndexOfMinimumTrace: [30x1 double] ObjectiveMinimumTrace: [30x1 double] EstimatedObjectiveMinimumTrace: [30x1 double] ```

This optimization technique is simpler than that shown in Find Good Lasso Penalty Using Cross-Validation, but does not allow you to trade off model complexity and cross-validation loss.

## Input Arguments

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Predictor data, specified as an n-by-p full or sparse matrix.

The length of `Y` and the number of observations in `X` must be equal.

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 optimization execution time.

Data Types: `single` | `double`

Response data, specified as an n-dimensional numeric vector. The length of `Y` must be equal to the number of observations in `X` or `Tbl`.

Data Types: `single` | `double`

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 one additional column for the response variable. Multicolumn variables and cell arrays other than cell arrays of character vectors are not allowed.

• If `Tbl` contains the response variable, and you want to use all remaining variables in `Tbl` as predictors, then specify the response variable by using `ResponseVarName`.

• If `Tbl` contains the response variable, and you want to use only a subset of the remaining variables in `Tbl` as predictors, then specify a formula by using `formula`.

• If `Tbl` does not contain the response variable, then specify a response variable by using `Y`. The length of the response variable and the number of rows in `Tbl` must be equal.

Data Types: `table`

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 `Tbl` stores the response variable `Y` as `Tbl.Y`, then specify it as `'Y'`. Otherwise, the software treats all columns of `Tbl`, including `Y`, as predictors when training the model.

Data Types: `char` | `string`

Explanatory model of the response variable and a subset of the predictor variables, specified as a character vector or string scalar in the form `"Y~x1+x2+x3"`. In this form, `Y` represents the response variable, and `x1`, `x2`, and `x3` represent the predictor variables.

To specify a subset of variables in `Tbl` as predictors for training the model, use a formula. If you specify a formula, then the software does not use any variables in `Tbl` that do not appear in `formula`.

The variable names in the formula must be both variable names in `Tbl` (`Tbl.Properties.VariableNames`) and valid MATLAB® identifiers. You can verify the variable names in `Tbl` by using the `isvarname` function. If the variable names are not valid, then you can convert them by using the `matlab.lang.makeValidName` function.

Data Types: `char` | `string`

Note

The software treats `NaN`, empty character vector (`''`), empty string (`""`), `<missing>`, and `<undefined>` elements as missing values, and removes observations with any of these characteristics:

• Missing value in the response (for example, `Y` or `ValidationData``{2}`)

• At least one missing value in a predictor observation (for example, row in `X` or `ValidationData{1}`)

• `NaN` value or `0` weight (for example, value in `Weights` or `ValidationData{3}`)

For memory-usage economy, it is best practice to remove observations containing missing values from your training data manually before training.

### 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`.

Example: `Mdl = fitrlinear(X,Y,'Learner','leastsquares','CrossVal','on','Regularization','lasso')` specifies to implement least-squares regression, implement 10-fold cross-validation, and specifies to include a lasso regularization term.

Note

You cannot use any cross-validation name-value argument together with the `'OptimizeHyperparameters'` name-value argument. You can modify the cross-validation for `'OptimizeHyperparameters'` only by using the `'HyperparameterOptimizationOptions'` name-value argument.

Linear Regression Options

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Half the width of the epsilon-insensitive band, specified as the comma-separated pair consisting of `'Epsilon'` and a nonnegative scalar value. `'Epsilon'` applies to SVM learners only.

The default `Epsilon` value is `iqr(Y)/13.49`, which is an estimate of standard deviation using the interquartile range of the response variable `Y`. If `iqr(Y)` is equal to zero, then the default `Epsilon` value is 0.1.

Example: `'Epsilon',0.3`

Data Types: `single` | `double`

Regularization term strength, specified as the comma-separated pair consisting of `'Lambda'` and `'auto'`, a nonnegative scalar, or a vector of nonnegative values.

• For `'auto'`, `Lambda` = 1/n.

• If you specify a cross-validation, name-value pair argument (e.g., `CrossVal`), then n is the number of in-fold observations.

• Otherwise, n is the training sample size.

• For a vector of nonnegative values, `fitrlinear` sequentially optimizes the objective function for each distinct value in `Lambda` in ascending order.

• If `Solver` is `'sgd'` or `'asgd'` and `Regularization` is `'lasso'`, `fitrlinear` does not use the previous coefficient estimates as a warm start for the next optimization iteration. Otherwise, `fitrlinear` uses warm starts.

• If `Regularization` is `'lasso'`, then any coefficient estimate of 0 retains its value when `fitrlinear` optimizes using subsequent values in `Lambda`.

• `fitrlinear` returns coefficient estimates for each specified regularization strength.

Example: `'Lambda',10.^(-(10:-2:2))`

Data Types: `char` | `string` | `double` | `single`

Linear regression model type, specified as the comma-separated pair consisting of `'Learner'` and `'svm'` or `'leastsquares'`.

In this 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.

ValueAlgorithmResponse rangeLoss function
`'leastsquares'`Linear regression via ordinary least squaresy ∊ (-∞,∞)Mean squared error (MSE): $\ell \left[y,f\left(x\right)\right]=\frac{1}{2}{\left[y-f\left(x\right)\right]}^{2}$
`'svm'`Support vector machine regressionSame as `'leastsquares'`Epsilon-insensitive: $\ell \left[y,f\left(x\right)\right]=\mathrm{max}\left[0,|y-f\left(x\right)|-\epsilon \right]$

Example: `'Learner','leastsquares'`

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.

Example: `'ObservationsIn','columns'`

Data Types: `char` | `string`

Complexity penalty type, specified as the comma-separated pair consisting of `'Regularization'` and `'lasso'` or `'ridge'`.

The software composes the objective function for minimization from the sum of the average loss function (see `Learner`) and the regularization term in this table.

ValueDescription
`'lasso'`Lasso (L1) penalty: $\lambda \sum _{j=1}^{p}|{\beta }_{j}|$
`'ridge'`Ridge (L2) penalty: $\frac{\lambda }{2}\sum _{j=1}^{p}{\beta }_{j}^{2}$

To specify the regularization term strength, which is λ in the expressions, use `Lambda`.

The software excludes the bias term (β0) from the regularization penalty.

If `Solver` is `'sparsa'`, then the default value of `Regularization` is `'lasso'`. Otherwise, the default is `'ridge'`.

Tip

• For predictor variable selection, specify `'lasso'`. For more on variable selection, see Introduction to Feature Selection.

• For optimization accuracy, specify `'ridge'`.

Example: `'Regularization','lasso'`

Objective function minimization technique, specified as the comma-separated pair consisting of `'Solver'` and a character vector or string scalar, a string array, or a cell array of character vectors with values from this table.

ValueDescriptionRestrictions
`'sgd'`Stochastic gradient descent (SGD) [5][3]
`'asgd'`Average stochastic gradient descent (ASGD) [8]
`'dual'`Dual SGD for SVM [2][7]`Regularization` must be `'ridge'` and `Learner` must be `'svm'`.
`'bfgs'`Broyden-Fletcher-Goldfarb-Shanno quasi-Newton algorithm (BFGS) [4]Inefficient if `X` is very high-dimensional.
`'lbfgs'`Limited-memory BFGS (LBFGS) [4]`Regularization` must be `'ridge'`.
`'sparsa'`Sparse Reconstruction by Separable Approximation (SpaRSA) [6]`Regularization` must be `'lasso'`.

If you specify:

• A ridge penalty (see `Regularization`) and `size(X,1) <= 100` (100 or fewer predictor variables), then the default solver is `'bfgs'`.

• An SVM regression model (see `Learner`), a ridge penalty, and `size(X,1) > 100` (more than 100 predictor variables), then the default solver is `'dual'`.

• A lasso penalty and `X` contains 100 or fewer predictor variables, then the default solver is `'sparsa'`.

Otherwise, the default solver is `'sgd'`.

If you specify a string array or cell array of solver names, then, for each value in `Lambda`, the software uses the solutions of solver j as a warm start for solver j + 1.

Example: `{'sgd' 'lbfgs'}` applies SGD to solve the objective, and uses the solution as a warm start for LBFGS.

Tip

• SGD and ASGD can solve the objective function more quickly than other solvers, whereas LBFGS and SpaRSA can yield more accurate solutions than other solvers. Solver combinations like `{'sgd' 'lbfgs'}` and ```{'sgd' 'sparsa'}``` can balance optimization speed and accuracy.

• When choosing between SGD and ASGD, consider that:

• SGD takes less time per iteration, but requires more iterations to converge.

• ASGD requires fewer iterations to converge, but takes more time per iteration.

• If the predictor data is high dimensional and `Regularization` is `'ridge'`, set `Solver` to any of these combinations:

• `'sgd'`

• `'asgd'`

• `'dual'` if `Learner` is `'svm'`

• `'lbfgs'`

• `{'sgd','lbfgs'}`

• `{'asgd','lbfgs'}`

• `{'dual','lbfgs'}` if `Learner` is `'svm'`

Although you can set other combinations, they often lead to solutions with poor accuracy.

• If the predictor data is moderate through low dimensional and `Regularization` is `'ridge'`, set `Solver` to `'bfgs'`.

• If `Regularization` is `'lasso'`, set `Solver` to any of these combinations:

• `'sgd'`

• `'asgd'`

• `'sparsa'`

• `{'sgd','sparsa'}`

• `{'asgd','sparsa'}`

Example: `'Solver',{'sgd','lbfgs'}`

Initial linear coefficient estimates (β), specified as the comma-separated pair consisting of `'Beta'` and a p-dimensional numeric vector or a p-by-L numeric matrix. p is the number of predictor variables in `X` and L is the number of regularization-strength values (for more details, see `Lambda`).

• If you specify a p-dimensional vector, then the software optimizes the objective function L times using this process.

1. The software optimizes using `Beta` as the initial value and the minimum value of `Lambda` as the regularization strength.

2. The software optimizes again using the resulting estimate from the previous optimization as a warm start, and the next smallest value in `Lambda` as the regularization strength.

3. The software implements step 2 until it exhausts all values in `Lambda`.

• If you specify a p-by-L matrix, then the software optimizes the objective function L times. At iteration `j`, the software uses `Beta(:,j)` as the initial value and, after it sorts `Lambda` in ascending order, uses `Lambda(j)` as the regularization strength.

If you set `'Solver','dual'`, then the software ignores `Beta`.

Data Types: `single` | `double`

Initial intercept estimate (b), specified as the comma-separated pair consisting of `'Bias'` and a numeric scalar or an L-dimensional numeric vector. L is the number of regularization-strength values (for more details, see `Lambda`).

• If you specify a scalar, then the software optimizes the objective function L times using this process.

1. The software optimizes using `Bias` as the initial value and the minimum value of `Lambda` as the regularization strength.

2. The uses the resulting estimate as a warm start to the next optimization iteration, and uses the next smallest value in `Lambda` as the regularization strength.

3. The software implements step 2 until it exhausts all values in `Lambda`.

• If you specify an L-dimensional vector, then the software optimizes the objective function L times. At iteration `j`, the software uses `Bias(j)` as the initial value and, after it sorts `Lambda` in ascending order, uses `Lambda(j)` as the regularization strength.

• By default:

• If `Learner` is `'leastsquares'`, then `Bias` is the weighted average of `Y` for training or, for cross-validation, in-fold responses.

• If `Learner` is `'svm'`, then `Bias` is the weighted median of `Y` for all training or, for cross-validation, in-fold observations that are greater than `Epsilon`.

Data Types: `single` | `double`

Linear model intercept inclusion flag, specified as the comma-separated pair consisting of `'FitBias'` and `true` or `false`.

ValueDescription
`true`The software includes the bias term b in the linear model, and then estimates it.
`false`The software sets b = 0 during estimation.

Example: `'FitBias',false`

Data Types: `logical`

Flag to fit the linear model intercept after optimization, specified as the comma-separated pair consisting of `'PostFitBias'` and `true` or `false`.

ValueDescription
`false`The software estimates the bias term b and the coefficients β during optimization.
`true`

To estimate b, the software:

1. Estimates β and b using the model.

2. Computes residuals.

3. Refits b. For least squares, b is the weighted average of the residuals. For SVM regression, b is the weighted median between all residuals with magnitude greater than `Epsilon`.

If you specify `true`, then `FitBias` must be true.

Example: `'PostFitBias',true`

Data Types: `logical`

Verbosity level, specified as the comma-separated pair consisting of `'Verbose'` and a nonnegative integer. `Verbose` controls the amount of diagnostic information `fitrlinear` displays at the command line.

ValueDescription
`0``fitrlinear` does not display diagnostic information.
`1``fitrlinear` periodically displays and stores the value of the objective function, gradient magnitude, and other diagnostic information. `FitInfo.History` contains the diagnostic information.
Any other positive integer`fitrlinear` displays and stores diagnostic information at each optimization iteration. `FitInfo.History` contains the diagnostic information.

Example: `'Verbose',1`

Data Types: `double` | `single`

SGD and ASGD Solver Options

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Mini-batch size, specified as the comma-separated pair consisting of `'BatchSize'` and a positive integer. At each iteration, the software estimates the subgradient using `BatchSize` observations from the training data.

• If `X` is a numeric matrix, then the default value is `10`.

• If `X` is a sparse matrix, then the default value is `max([10,ceil(sqrt(ff))])`, where `ff = numel(X)/nnz(X)` (the fullness factor of `X`).

Example: `'BatchSize',100`

Data Types: `single` | `double`

Learning rate, specified as the comma-separated pair consisting of `'LearnRate'` and a positive scalar. `LearnRate` specifies how many steps to take per iteration. At each iteration, the gradient specifies the direction and magnitude of each step.

• If `Regularization` is `'ridge'`, then `LearnRate` specifies the initial learning rate γ0. The software determines the learning rate for iteration t, γt, using

`${\gamma }_{t}=\frac{{\gamma }_{0}}{{\left(1+\lambda {\gamma }_{0}t\right)}^{c}}.$`

• If `Regularization` is `'lasso'`, then, for all iterations, `LearnRate` is constant.

By default, `LearnRate` is `1/sqrt(1+max((sum(X.^2,obsDim))))`, where `obsDim` is `1` if the observations compose the columns of `X`, and `2` otherwise.

Example: `'LearnRate',0.01`

Data Types: `single` | `double`

Flag to decrease the learning rate when the software detects divergence (that is, over-stepping the minimum), specified as the comma-separated pair consisting of `'OptimizeLearnRate'` and `true` or `false`.

If `OptimizeLearnRate` is `'true'`, then:

1. For the few optimization iterations, the software starts optimization using `LearnRate` as the learning rate.

2. If the value of the objective function increases, then the software restarts and uses half of the current value of the learning rate.

3. The software iterates step 2 until the objective function decreases.

Example: `'OptimizeLearnRate',true`

Data Types: `logical`

Number of mini-batches between lasso truncation runs, specified as the comma-separated pair consisting of `'TruncationPeriod'` and a positive integer.

After a truncation run, the software applies a soft threshold to the linear coefficients. That is, after processing k = `TruncationPeriod` mini-batches, the software truncates the estimated coefficient j using

`${\stackrel{^}{\beta }}_{j}^{\ast }=\left\{\begin{array}{ll}{\stackrel{^}{\beta }}_{j}-{u}_{t}\hfill & \text{if}\text{\hspace{0.17em}}{\stackrel{^}{\beta }}_{j}>{u}_{t},\hfill \\ 0\hfill & \text{if}\text{\hspace{0.17em}}|{\stackrel{^}{\beta }}_{j}|\le {u}_{t},\hfill \\ {\stackrel{^}{\beta }}_{j}+{u}_{t}\hfill & \text{if}\text{\hspace{0.17em}}{\stackrel{^}{\beta }}_{j}<-{u}_{t}.\hfill \end{array}\begin{array}{r}\hfill \text{\hspace{0.17em}}\text{\hspace{0.17em}}\\ \hfill \text{\hspace{0.17em}}\text{\hspace{0.17em}}\\ \hfill \text{\hspace{0.17em}}\text{\hspace{0.17em}}\end{array}$`

• For SGD, ${\stackrel{^}{\beta }}_{j}$ is the estimate of coefficient j after processing k mini-batches. ${u}_{t}=k{\gamma }_{t}\lambda .$ γt is the learning rate at iteration t. λ is the value of `Lambda`.

• For ASGD, ${\stackrel{^}{\beta }}_{j}$ is the averaged estimate coefficient j after processing k mini-batches, ${u}_{t}=k\lambda .$

If `Regularization` is `'ridge'`, then the software ignores `TruncationPeriod`.

Example: `'TruncationPeriod',100`

Data Types: `single` | `double`

Other Regression Options

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Categorical predictors list, specified as one of the values in this table. The descriptions assume that the predictor data has observations in rows and predictors in columns.

ValueDescription
Vector of positive integers

Each entry in the vector is an index value indicating that the corresponding predictor is categorical. The index values are between 1 and `p`, where `p` is the number of predictors used to train the model.

If `fitrlinear` uses a subset of input variables as predictors, then the function indexes the predictors using only the subset. The `CategoricalPredictors` values do not count the response variable, observation weight variable, or any other variables that the function does not use.

Logical vector

A `true` entry means that the corresponding predictor is categorical. The length of the vector is `p`.

Character matrixEach row of the matrix is the name of a predictor variable. The names must match the entries in `PredictorNames`. Pad the names with extra blanks so each row of the character matrix has the same length.
String array or cell array of character vectorsEach element in the array is the name of a predictor variable. The names must match the entries in `PredictorNames`.
`"all"`All predictors are categorical.

By default, if the predictor data is in a table (`Tbl`), `fitrlinear` assumes that a variable is categorical if it is a logical vector, categorical vector, character array, string array, or cell array of character vectors. If the predictor data is a matrix (`X`), `fitrlinear` assumes that all predictors are continuous. To identify any other predictors as categorical predictors, specify them by using the `'CategoricalPredictors'` name-value argument.

For the identified categorical predictors, `fitrlinear` creates dummy variables using two different schemes, depending on whether a categorical variable is unordered or ordered. For an unordered categorical variable, `fitrlinear` creates one dummy variable for each level of the categorical variable. For an ordered categorical variable, `fitrlinear` creates one less dummy variable than the number of categories. For details, see Automatic Creation of Dummy Variables.

Example: `'CategoricalPredictors','all'`

Data Types: `single` | `double` | `logical` | `char` | `string` | `cell`

Predictor variable names, specified as a string array of unique names or cell array of unique character vectors. The functionality of `'PredictorNames'` depends on the way you supply the training data.

• If you supply `X` and `Y`, then you can use `'PredictorNames'` to assign names to the predictor variables in `X`.

• The order of the names in `PredictorNames` must correspond to the predictor order in `X`. Assuming that `X` has the default orientation, with observations in rows and predictors in columns, `PredictorNames{1}` is the name of `X(:,1)`, `PredictorNames{2}` is the name of `X(:,2)`, and so on. Also, `size(X,2)` and `numel(PredictorNames)` must be equal.

• By default, `PredictorNames` is `{'x1','x2',...}`.

• If you supply `Tbl`, then you can use `'PredictorNames'` to choose which predictor variables to use in training. That is, `fitrlinear` uses only the predictor variables in `PredictorNames` and the response variable during training.

• `PredictorNames` must be a subset of `Tbl.Properties.VariableNames` and cannot include the name of the response variable.

• By default, `PredictorNames` contains the names of all predictor variables.

• A good practice is to specify the predictors for training using either `'PredictorNames'` or `formula`, but not both.

Example: `'PredictorNames',{'SepalLength','SepalWidth','PetalLength','PetalWidth'}`

Data Types: `string` | `cell`

Response variable name, specified as a character vector or string scalar.

Example: `"ResponseName","response"`

Data Types: `char` | `string`

Response transformation, specified as either `'none'` or a function handle. The default is `'none'`, which means `@(y)y`, or no transformation. For a MATLAB function or a function you define, use its function handle for the response transformation. The function handle must accept a vector (the original response values) and return a vector of the same size (the transformed response values).

Example: Suppose you create a function handle that applies an exponential transformation to an input vector by using `myfunction = @(y)exp(y)`. Then, you can specify the response transformation as `'ResponseTransform',myfunction`.

Data Types: `char` | `string` | `function_handle`

Observation weights, specified as the comma-separated pair consisting of `'Weights'` and a positive 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 weights vector `W` is stored as `Tbl.W`, then specify it as `'W'`. Otherwise, the software treats all columns of `Tbl`, including `W`, as predictors when training the model.

By default, `Weights` is `ones(n,1)`, where `n` is the number of observations in `X` or `Tbl`.

`fitrlinear` normalizes the weights to sum to 1.

Data Types: `single` | `double` | `char` | `string`

Cross-Validation Options

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Cross-validation flag, specified as the comma-separated pair consisting of `'Crossval'` and `'on'` or `'off'`.

If you specify `'on'`, then the software implements 10-fold cross-validation.

To override this cross-validation setting, use one of these name-value pair arguments: `CVPartition`, `Holdout`, or `KFold`. To create a cross-validated model, you can use one cross-validation name-value pair argument at a time only.

Example: `'Crossval','on'`

Cross-validation partition, specified as the comma-separated pair consisting of `'CVPartition'` and a `cvpartition` partition object as created by `cvpartition`. The partition object specifies the type of cross-validation, and also the indexing for training and validation sets.

To create a cross-validated model, you can use one of these four options only: `'``CVPartition``'`, `'``Holdout``'`, or `'``KFold``'`.

Fraction of data used for holdout validation, specified as the comma-separated pair consisting of `'Holdout'` and a scalar value in the range (0,1). If you specify `'Holdout',p`, then the software:

1. Randomly reserves `p*100`% of the data as validation data, and trains the model using the rest of the data

2. Stores the compact, trained model in the `Trained` property of the cross-validated model.

To create a cross-validated model, you can use one of these four options only: `'``CVPartition``'`, `'``Holdout``'`, or `'``KFold``'`.

Example: `'Holdout',0.1`

Data Types: `double` | `single`

Number of folds to use in a cross-validated classifier, specified as the comma-separated pair consisting of `'KFold'` and a positive integer value greater than 1. If you specify, e.g., `'KFold',k`, then the software:

1. Randomly partitions the data into k sets

2. For each set, reserves the set as validation data, and trains the model using the other k – 1 sets

3. Stores the `k` compact, trained models in the cells of a `k`-by-1 cell vector in the `Trained` property of the cross-validated model.

To create a cross-validated model, you can use one of these four options only: `'``CVPartition``'`, `'``Holdout``'`, or `'``KFold``'`.

Example: `'KFold',8`

Data Types: `single` | `double`

SGD and ASGD Convergence Controls

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Maximal number of batches to process, specified as the comma-separated pair consisting of `'BatchLimit'` and a positive integer. When the software processes `BatchLimit` batches, it terminates optimization.

• By default:

• The software passes through the data `PassLimit` times.

• If you specify multiple solvers, and use (A)SGD to get an initial approximation for the next solver, then the default value is `ceil(1e6/BatchSize)`. `BatchSize` is the value of the `'``BatchSize``'` name-value pair argument.

• If you specify `'BatchLimit'` and `'``PassLimit``'`, then the software chooses the argument that results in processing the fewest observations.

• If you specify `'BatchLimit'` but not `'PassLimit'`, then the software processes enough batches to complete up to one entire pass through the data.

Example: `'BatchLimit',100`

Data Types: `single` | `double`

Relative tolerance on the linear coefficients and the bias term (intercept), specified as the comma-separated pair consisting of `'BetaTolerance'` and a nonnegative scalar.

Let ${B}_{t}=\left[{\beta }_{t}{}^{\prime }\text{\hspace{0.17em}}\text{\hspace{0.17em}}{b}_{t}\right]$, that is, the vector of the coefficients and the bias term at optimization iteration t. If ${‖\frac{{B}_{t}-{B}_{t-1}}{{B}_{t}}‖}_{2}<\text{BetaTolerance}$, then optimization terminates.

If the software converges for the last solver specified in `Solver`, then optimization terminates. Otherwise, the software uses the next solver specified in `Solver`.

Example: `'BetaTolerance',1e-6`

Data Types: `single` | `double`

Number of batches to process before next convergence check, specified as the comma-separated pair consisting of `'NumCheckConvergence'` and a positive integer.

To specify the batch size, see `BatchSize`.

The software checks for convergence about 10 times per pass through the entire data set by default.

Example: `'NumCheckConvergence',100`

Data Types: `single` | `double`

Maximal number of passes through the data, specified as the comma-separated pair consisting of `'PassLimit'` and a positive integer.

`fitrlinear` processes all observations when it completes one pass through the data.

When `fitrlinear` passes through the data `PassLimit` times, it terminates optimization.

If you specify `'``BatchLimit``'` and `PassLimit`, then `fitrlinear` chooses the argument that results in processing the fewest observations. For more details, see Algorithms.

Example: `'PassLimit',5`

Data Types: `single` | `double`

Validation data for optimization convergence detection, specified as the comma-separated pair consisting of `'ValidationData'` and a cell array or table.

During optimization, the software periodically estimates the loss of `ValidationData`. If the validation-data loss increases, then the software terminates optimization. For more details, see Algorithms. To optimize hyperparameters using cross-validation, see cross-validation options such as `CrossVal`.

You can specify `ValidationData` as a table if you use a table `Tbl` of predictor data that contains the response variable. In this case, `ValidationData` must contain the same predictors and response contained in `Tbl`. The software does not apply weights to observations, even if `Tbl` contains a vector of weights. To specify weights, you must specify `ValidationData` as a cell array.

If you specify `ValidationData` as a cell array, then it must have the following format:

• `ValidationData{1}` must have the same data type and orientation as the predictor data. That is, if you use a predictor matrix `X`, then `ValidationData{1}` must be an m-by-p or p-by-m full or sparse matrix of predictor data that has the same orientation as `X`. The predictor variables in the training data `X` and `ValidationData{1}` must correspond. Similarly, if you use a predictor table `Tbl` of predictor data, then `ValidationData{1}` must be a table containing the same predictor variables contained in `Tbl`. The number of observations in `ValidationData{1}` and the predictor data can vary.

• `ValidationData{2}` must match the data type and format of the response variable, either `Y` or `ResponseVarName`. If `ValidationData{2}` is an array of responses, then it must have the same number of elements as the number of observations in `ValidationData{1}`. If `ValidationData{1}` is a table, then `ValidationData{2}` can be the name of the response variable in the table. If you want to use the same `ResponseVarName` or `formula`, you can specify `ValidationData{2}` as `[]`.

• Optionally, you can specify `ValidationData{3}` as an m-dimensional numeric vector of observation weights or the name of a variable in the table `ValidationData{1}` that contains observation weights. The software normalizes the weights with the validation data so that they sum to 1.

If you specify `ValidationData` and want to display the validation loss at the command line, specify a value larger than 0 for `Verbose`.

If the software converges for the last solver specified in `Solver`, then optimization terminates. Otherwise, the software uses the next solver specified in `Solver`.

By default, the software does not detect convergence by monitoring validation-data loss.

Absolute gradient tolerance, specified as the comma-separated pair consisting of `'GradientTolerance'` and a nonnegative scalar. `GradientTolerance` applies to these values of `Solver`: `'bfgs'`, `'lbfgs'`, and `'sparsa'`.

Let $\nabla {ℒ}_{t}$ be the gradient vector of the objective function with respect to the coefficients and bias term at optimization iteration t. If ${‖\nabla {ℒ}_{t}‖}_{\infty }=\mathrm{max}|\nabla {ℒ}_{t}|<\text{GradientTolerance}$, then optimization terminates.

If you also specify `BetaTolerance`, then optimization terminates when `fitrlinear` satisfies either stopping criterion.

If `fitrlinear` converges for the last solver specified in `Solver`, then optimization terminates. Otherwise, `fitrlinear` uses the next solver specified in `Solver`.

Example: `'GradientTolerance',eps`

Data Types: `single` | `double`

Maximal number of optimization iterations, specified as the comma-separated pair consisting of `'IterationLimit'` and a positive integer. `IterationLimit` applies to these values of `Solver`: `'bfgs'`, `'lbfgs'`, and `'sparsa'`.

Example: `'IterationLimit',1e7`

Data Types: `single` | `double`

Dual SGD Optimization Convergence Controls

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Relative tolerance on the linear coefficients and the bias term (intercept), specified as the comma-separated pair consisting of `'BetaTolerance'` and a nonnegative scalar.

Let ${B}_{t}=\left[{\beta }_{t}{}^{\prime }\text{\hspace{0.17em}}\text{\hspace{0.17em}}{b}_{t}\right]$, that is, the vector of the coefficients and the bias term at optimization iteration t. If ${‖\frac{{B}_{t}-{B}_{t-1}}{{B}_{t}}‖}_{2}<\text{BetaTolerance}$, then optimization terminates.

If you also specify `DeltaGradientTolerance`, then optimization terminates when the software satisfies either stopping criterion.

If the software converges for the last solver specified in `Solver`, then optimization terminates. Otherwise, the software uses the next solver specified in `Solver`.

Example: `'BetaTolerance',1e-6`

Data Types: `single` | `double`

Gradient-difference tolerance between upper and lower pool Karush-Kuhn-Tucker (KKT) complementarity conditions violators, specified as the comma-separated pair consisting of `'DeltaGradientTolerance'` and a nonnegative scalar. `DeltaGradientTolerance` applies to the `'dual'` value of `Solver` only.

• If the magnitude of the KKT violators is less than `DeltaGradientTolerance`, then `fitrlinear` terminates optimization.

• If `fitrlinear` converges for the last solver specified in `Solver`, then optimization terminates. Otherwise, `fitrlinear` uses the next solver specified in `Solver`.

Example: `'DeltaGapTolerance',1e-2`

Data Types: `double` | `single`

Number of passes through entire data set to process before next convergence check, specified as the comma-separated pair consisting of `'NumCheckConvergence'` and a positive integer.

Example: `'NumCheckConvergence',100`

Data Types: `single` | `double`

Maximal number of passes through the data, specified as the comma-separated pair consisting of `'PassLimit'` and a positive integer.

When the software completes one pass through the data, it has processed all observations.

When the software passes through the data `PassLimit` times, it terminates optimization.

Example: `'PassLimit',5`

Data Types: `single` | `double`

Validation data for optimization convergence detection, specified as the comma-separated pair consisting of `'ValidationData'` and a cell array or table.

During optimization, the software periodically estimates the loss of `ValidationData`. If the validation-data loss increases, then the software terminates optimization. For more details, see Algorithms. To optimize hyperparameters using cross-validation, see cross-validation options such as `CrossVal`.

You can specify `ValidationData` as a table if you use a table `Tbl` of predictor data that contains the response variable. In this case, `ValidationData` must contain the same predictors and response contained in `Tbl`. The software does not apply weights to observations, even if `Tbl` contains a vector of weights. To specify weights, you must specify `ValidationData` as a cell array.

If you specify `ValidationData` as a cell array, then it must have the following format:

• `ValidationData{1}` must have the same data type and orientation as the predictor data. That is, if you use a predictor matrix `X`, then `ValidationData{1}` must be an m-by-p or p-by-m full or sparse matrix of predictor data that has the same orientation as `X`. The predictor variables in the training data `X` and `ValidationData{1}` must correspond. Similarly, if you use a predictor table `Tbl` of predictor data, then `ValidationData{1}` must be a table containing the same predictor variables contained in `Tbl`. The number of observations in `ValidationData{1}` and the predictor data can vary.

• `ValidationData{2}` must match the data type and format of the response variable, either `Y` or `ResponseVarName`. If `ValidationData{2}` is an array of responses, then it must have the same number of elements as the number of observations in `ValidationData{1}`. If `ValidationData{1}` is a table, then `ValidationData{2}` can be the name of the response variable in the table. If you want to use the same `ResponseVarName` or `formula`, you can specify `ValidationData{2}` as `[]`.

• Optionally, you can specify `ValidationData{3}` as an m-dimensional numeric vector of observation weights or the name of a variable in the table `ValidationData{1}` that contains observation weights. The software normalizes the weights with the validation data so that they sum to 1.

If you specify `ValidationData` and want to display the validation loss at the command line, specify a value larger than 0 for `Verbose`.

If the software converges for the last solver specified in `Solver`, then optimization terminates. Otherwise, the software uses the next solver specified in `Solver`.

By default, the software does not detect convergence by monitoring validation-data loss.

BFGS, LBFGS, and SpaRSA Convergence Controls

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Relative tolerance on the linear coefficients and the bias term (intercept), specified as the comma-separated pair consisting of `'BetaTolerance'` and a nonnegative scalar.

Let ${B}_{t}=\left[{\beta }_{t}{}^{\prime }\text{\hspace{0.17em}}\text{\hspace{0.17em}}{b}_{t}\right]$, that is, the vector of the coefficients and the bias term at optimization iteration t. If ${‖\frac{{B}_{t}-{B}_{t-1}}{{B}_{t}}‖}_{2}<\text{BetaTolerance}$, then optimization terminates.

If you also specify `GradientTolerance`, then optimization terminates when the software satisfies either stopping criterion.

If the software converges for the last solver specified in `Solver`, then optimization terminates. Otherwise, the software uses the next solver specified in `Solver`.

Example: `'BetaTolerance',1e-6`

Data Types: `single` | `double`

Absolute gradient tolerance, specified as the comma-separated pair consisting of `'GradientTolerance'` and a nonnegative scalar.

Let $\nabla {ℒ}_{t}$ be the gradient vector of the objective function with respect to the coefficients and bias term at optimization iteration t. If ${‖\nabla {ℒ}_{t}‖}_{\infty }=\mathrm{max}|\nabla {ℒ}_{t}|<\text{GradientTolerance}$, then optimization terminates.

If you also specify `BetaTolerance`, then optimization terminates when the software satisfies either stopping criterion.

If the software converges for the last solver specified in the software, then optimization terminates. Otherwise, the software uses the next solver specified in `Solver`.

Example: `'GradientTolerance',1e-5`

Data Types: `single` | `double`

Size of history buffer for Hessian approximation, specified as the comma-separated pair consisting of `'HessianHistorySize'` and a positive integer. That is, at each iteration, the software composes the Hessian using statistics from the latest `HessianHistorySize` iterations.

The software does not support `'HessianHistorySize'` for SpaRSA.

Example: `'HessianHistorySize',10`

Data Types: `single` | `double`

Maximal number of optimization iterations, specified as the comma-separated pair consisting of `'IterationLimit'` and a positive integer. `IterationLimit` applies to these values of `Solver`: `'bfgs'`, `'lbfgs'`, and `'sparsa'`.

Example: `'IterationLimit',500`

Data Types: `single` | `double`

Validation data for optimization convergence detection, specified as the comma-separated pair consisting of `'ValidationData'` and a cell array or table.

During optimization, the software periodically estimates the loss of `ValidationData`. If the validation-data loss increases, then the software terminates optimization. For more details, see Algorithms. To optimize hyperparameters using cross-validation, see cross-validation options such as `CrossVal`.

You can specify `ValidationData` as a table if you use a table `Tbl` of predictor data that contains the response variable. In this case, `ValidationData` must contain the same predictors and response contained in `Tbl`. The software does not apply weights to observations, even if `Tbl` contains a vector of weights. To specify weights, you must specify `ValidationData` as a cell array.

If you specify `ValidationData` as a cell array, then it must have the following format:

• `ValidationData{1}` must have the same data type and orientation as the predictor data. That is, if you use a predictor matrix `X`, then `ValidationData{1}` must be an m-by-p or p-by-m full or sparse matrix of predictor data that has the same orientation as `X`. The predictor variables in the training data `X` and `ValidationData{1}` must correspond. Similarly, if you use a predictor table `Tbl` of predictor data, then `ValidationData{1}` must be a table containing the same predictor variables contained in `Tbl`. The number of observations in `ValidationData{1}` and the predictor data can vary.

• `ValidationData{2}` must match the data type and format of the response variable, either `Y` or `ResponseVarName`. If `ValidationData{2}` is an array of responses, then it must have the same number of elements as the number of observations in `ValidationData{1}`. If `ValidationData{1}` is a table, then `ValidationData{2}` can be the name of the response variable in the table. If you want to use the same `ResponseVarName` or `formula`, you can specify `ValidationData{2}` as `[]`.

• Optionally, you can specify `ValidationData{3}` as an m-dimensional numeric vector of observation weights or the name of a variable in the table `ValidationData{1}` that contains observation weights. The software normalizes the weights with the validation data so that they sum to 1.

If you specify `ValidationData` and want to display the validation loss at the command line, specify a value larger than 0 for `Verbose`.

If the software converges for the last solver specified in `Solver`, then optimization terminates. Otherwise, the software uses the next solver specified in `Solver`.

By default, the software does not detect convergence by monitoring validation-data loss.

Hyperparameter Optimization

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Parameters to optimize, specified as the comma-separated pair consisting of `'OptimizeHyperparameters'` and one of the following:

• `'none'` — Do not optimize.

• `'auto'` — Use `{'Lambda','Learner'}`.

• `'all'` — Optimize all eligible parameters.

• String array or cell array of eligible parameter names.

• Vector of `optimizableVariable` objects, typically the output of `hyperparameters`.

The optimization attempts to minimize the cross-validation loss (error) for `fitrlinear` by varying the parameters. To control the cross-validation type and other aspects of the optimization, use the `HyperparameterOptimizationOptions` name-value pair.

Note

The values of `'OptimizeHyperparameters'` override any values you specify using other name-value arguments. For example, setting `'OptimizeHyperparameters'` to `'auto'` causes `fitrlinear` to optimize hyperparameters corresponding to the `'auto'` option and to ignore any specified values for the hyperparameters.

The eligible parameters for `fitrlinear` are:

• `Lambda``fitrlinear` searches among positive values, by default log-scaled in the range `[1e-5/NumObservations,1e5/NumObservations]`.

• `Learner``fitrlinear` searches among `'svm'` and `'leastsquares'`.

• `Regularization``fitrlinear` searches among `'ridge'` and `'lasso'`.

Set nondefault parameters by passing a vector of `optimizableVariable` objects that have nondefault values. For example,

```load carsmall params = hyperparameters('fitrlinear',[Horsepower,Weight],MPG); params(1).Range = [1e-3,2e4];```

Pass `params` as the value of `OptimizeHyperparameters`.

By default, the iterative display appears at the command line, and plots appear according to the number of hyperparameters in the optimization. For the optimization and plots, the objective function is log(1 + cross-validation loss). To control the iterative display, set the `Verbose` field of the `'HyperparameterOptimizationOptions'` name-value argument. To control the plots, set the `ShowPlots` field of the `'HyperparameterOptimizationOptions'` name-value argument.

For an example, see Optimize a Linear Regression.

Example: `'OptimizeHyperparameters','auto'`

Options for optimization, specified as a structure. This argument modifies the effect of the `OptimizeHyperparameters` name-value argument. All fields in the structure are optional.

Field NameValuesDefault
`Optimizer`
• `'bayesopt'` — Use Bayesian optimization. Internally, this setting calls `bayesopt`.

• `'gridsearch'` — Use grid search with `NumGridDivisions` values per dimension.

• `'randomsearch'` — Search at random among `MaxObjectiveEvaluations` points.

`'gridsearch'` searches in a random order, using uniform sampling without replacement from the grid. After optimization, you can get a table in grid order by using the command `sortrows(Mdl.HyperparameterOptimizationResults)`.

`'bayesopt'`
`AcquisitionFunctionName`

• `'expected-improvement-per-second-plus'`

• `'expected-improvement'`

• `'expected-improvement-plus'`

• `'expected-improvement-per-second'`

• `'lower-confidence-bound'`

• `'probability-of-improvement'`

Acquisition functions whose names include `per-second` do not yield reproducible results because the optimization depends on the runtime of the objective function. Acquisition functions whose names include `plus` modify their behavior when they are overexploiting an area. For more details, see Acquisition Function Types.

`'expected-improvement-per-second-plus'`
`MaxObjectiveEvaluations`Maximum number of objective function evaluations.`30` for `'bayesopt'` and `'randomsearch'`, and the entire grid for `'gridsearch'`
`MaxTime`

Time limit, specified as a positive real scalar. The time limit is in seconds, as measured by `tic` and `toc`. The run time can exceed `MaxTime` because `MaxTime` does not interrupt function evaluations.

`Inf`
`NumGridDivisions`For `'gridsearch'`, the number of values in each dimension. The value can be a vector of positive integers giving the number of values for each dimension, or a scalar that applies to all dimensions. This field is ignored for categorical variables.`10`
`ShowPlots`Logical value indicating whether to show plots. If `true`, this field plots the best observed objective function value against the iteration number. If you use Bayesian optimization (`Optimizer` is `'bayesopt'`), then this field also plots the best estimated objective function value. The best observed objective function values and best estimated objective function values correspond to the values in the `BestSoFar (observed)` and ```BestSoFar (estim.)``` columns of the iterative display, respectively. You can find these values in the properties `ObjectiveMinimumTrace` and `EstimatedObjectiveMinimumTrace` of `Mdl.HyperparameterOptimizationResults`. If the problem includes one or two optimization parameters for Bayesian optimization, then `ShowPlots` also plots a model of the objective function against the parameters.`true`
`SaveIntermediateResults`Logical value indicating whether to save results when `Optimizer` is `'bayesopt'`. If `true`, this field overwrites a workspace variable named `'BayesoptResults'` at each iteration. The variable is a `BayesianOptimization` object.`false`
`Verbose`

Display at the command line:

• `0` — No iterative display

• `1` — Iterative display

• `2` — Iterative display with extra information

For details, see the `bayesopt` `Verbose` name-value argument and the example Optimize Classifier Fit Using Bayesian Optimization.

`1`
`UseParallel`Logical value indicating whether to run Bayesian optimization in parallel, which requires Parallel Computing Toolbox™. Due to the nonreproducibility of parallel timing, parallel Bayesian optimization does not necessarily yield reproducible results. For details, see Parallel Bayesian Optimization.`false`
`Repartition`

Logical value indicating whether to repartition the cross-validation at every iteration. If this field is `false`, the optimizer uses a single partition for the optimization.

The setting `true` usually gives the most robust results because it takes partitioning noise into account. However, for good results, `true` requires at least twice as many function evaluations.

`false`
Use no more than one of the following three options.
`CVPartition`A `cvpartition` object, as created by `cvpartition``'Kfold',5` if you do not specify a cross-validation field
`Holdout`A scalar in the range `(0,1)` representing the holdout fraction
`Kfold`An integer greater than 1

Example: `'HyperparameterOptimizationOptions',struct('MaxObjectiveEvaluations',60)`

Data Types: `struct`

## Output Arguments

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Trained linear regression model, returned as a `RegressionLinear` model object or `RegressionPartitionedLinear` cross-validated model object.

If you set any of the name-value pair arguments `KFold`, `Holdout`, `CrossVal`, or `CVPartition`, then `Mdl` is a `RegressionPartitionedLinear` cross-validated model object. Otherwise, `Mdl` is a `RegressionLinear` model object.

To reference properties of `Mdl`, use dot notation. For example, enter `Mdl.Beta` in the Command Window to display the vector or matrix of estimated coefficients.

Note

Unlike other regression models, and for economical memory usage, `RegressionLinear` and `RegressionPartitionedLinear` model objects do not store the training data or optimization details (for example, convergence history).

Optimization details, returned as a structure array.

Fields specify final values or name-value pair argument specifications, for example, `Objective` is the value of the objective function when optimization terminates. Rows of multidimensional fields correspond to values of `Lambda` and columns correspond to values of `Solver`.

This table describes some notable fields.

FieldDescription
`TerminationStatus`
• Reason for optimization termination

• Corresponds to a value in `TerminationCode`

`FitTime`Elapsed, wall-clock time in seconds
`History`

A structure array of optimization information for each iteration. The field `Solver` stores solver types using integer coding.

IntegerSolver
1SGD
2ASGD
3Dual SGD for SVM
4LBFGS
5BFGS
6SpaRSA

To access fields, use dot notation. For example, to access the vector of objective function values for each iteration, enter `FitInfo.History.Objective`.

It is good practice to examine `FitInfo` to assess whether convergence is satisfactory.

Cross-validation optimization of hyperparameters, returned as a `BayesianOptimization` object or a table of hyperparameters and associated values. The output is nonempty when the value of `'OptimizeHyperparameters'` is not `'none'`. The output value depends on the `Optimizer` field value of the `'HyperparameterOptimizationOptions'` name-value pair argument:

Value of `Optimizer` FieldValue of `HyperparameterOptimizationResults`
`'bayesopt'` (default)Object of class `BayesianOptimization`
`'gridsearch'` or `'randomsearch'`Table of hyperparameters used, observed objective function values (cross-validation loss), and rank of observations from lowest (best) to highest (worst)

Note

If `Learner` is `'leastsquares'`, then the loss term in the objective function is half of the MSE. `loss` returns the MSE by default. Therefore, if you use `loss` to check the resubstitution, or training, error then there is a discrepancy between the MSE returned by `loss` and optimization results in `FitInfo` or returned to the command line by setting a positive verbosity level using `Verbose`.

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### Warm Start

A warm start is initial estimates of the beta coefficients and bias term supplied to an optimization routine for quicker convergence.

### Alternatives for Lower-Dimensional Data

High-dimensional linear classification and regression models minimize objective functions relatively quickly, but at the cost of some accuracy, the numeric-only predictor variables restriction, and the model must be linear with respect to the parameters. If your predictor data set is low- through medium-dimensional, or contains heterogeneous variables, then you should use the appropriate classification or regression fitting function. To help you decide which fitting function is appropriate for your low-dimensional data set, use this table.

Model to FitFunctionNotable Algorithmic Differences
SVM
• Computes the Gram matrix of the predictor variables, which is convenient for nonlinear kernel transformations.

• Solves dual problem using SMO, ISDA, or L1 minimization via quadratic programming using `quadprog` (Optimization Toolbox).

Linear regression
• `lasso` implements cyclic coordinate descent.

Logistic regression
• `fitglm` implements iteratively reweighted least squares.

• `lassoglm` implements cyclic coordinate descent.

## Tips

• It is a best practice to orient your predictor matrix so that observations correspond to columns and to specify `'ObservationsIn','columns'`. As a result, you can experience a significant reduction in optimization-execution time.

• If your predictor data has few observations but many predictor variables, then:

• Specify `'PostFitBias',true`.

• For SGD or ASGD solvers, set `PassLimit` to a positive integer that is greater than 1, for example, 5 or 10. This setting often results in better accuracy.

• For SGD and ASGD solvers, `BatchSize` affects the rate of convergence.

• If `BatchSize` is too small, then `fitrlinear` achieves the minimum in many iterations, but computes the gradient per iteration quickly.

• If `BatchSize` is too large, then `fitrlinear` achieves the minimum in fewer iterations, but computes the gradient per iteration slowly.

• Large learning rates (see `LearnRate`) speed up convergence to the minimum, but can lead to divergence (that is, over-stepping the minimum). Small learning rates ensure convergence to the minimum, but can lead to slow termination.

• When using lasso penalties, experiment with various values of `TruncationPeriod`. For example, set `TruncationPeriod` to `1`, `10`, and then `100`.

• For efficiency, `fitrlinear` does not standardize predictor data. To standardize `X`, enter

`X = bsxfun(@rdivide,bsxfun(@minus,X,mean(X,2)),std(X,0,2));`

The code requires that you orient the predictors and observations as the rows and columns of `X`, respectively. Also, for memory-usage economy, the code replaces the original predictor data the standardized data.

• After training a model, you can generate C/C++ code that predicts responses for new data. Generating C/C++ code requires MATLAB Coder™. For details, see Introduction to Code Generation.

## Algorithms

• If you specify `ValidationData`, then, during objective-function optimization:

• `fitrlinear` estimates the validation loss of `ValidationData` periodically using the current model, and tracks the minimal estimate.

• When `fitrlinear` estimates a validation loss, it compares the estimate to the minimal estimate.

• When subsequent, validation loss estimates exceed the minimal estimate five times, `fitrlinear` terminates optimization.

• If you specify `ValidationData` and to implement a cross-validation routine (`CrossVal`, `CVPartition`, `Holdout`, or `KFold`), then:

1. `fitrlinear` randomly partitions `X` and `Y` (or `Tbl`) according to the cross-validation routine that you choose.

2. `fitrlinear` trains the model using the training-data partition. During objective-function optimization, `fitrlinear` uses `ValidationData` as another possible way to terminate optimization (for details, see the previous bullet).

3. Once `fitrlinear` satisfies a stopping criterion, it constructs a trained model based on the optimized linear coefficients and intercept.

1. If you implement k-fold cross-validation, and `fitrlinear` has not exhausted all training-set folds, then `fitrlinear` returns to Step 2 to train using the next training-set fold.

2. Otherwise, `fitrlinear` terminates training, and then returns the cross-validated model.

4. You can determine the quality of the cross-validated model. For example:

• To determine the validation loss using the holdout or out-of-fold data from step 1, pass the cross-validated model to `kfoldLoss`.

• To predict observations on the holdout or out-of-fold data from step 1, pass the cross-validated model to `kfoldPredict`.

## References

[1] Ho, C. H. and C. J. Lin. “Large-Scale Linear Support Vector Regression.” Journal of Machine Learning Research, Vol. 13, 2012, pp. 3323–3348.

[2] Hsieh, C. J., K. W. Chang, C. J. Lin, S. S. Keerthi, and S. Sundararajan. “A Dual Coordinate Descent Method for Large-Scale Linear SVM.” Proceedings of the 25th International Conference on Machine Learning, ICML ’08, 2001, pp. 408–415.

[3] Langford, J., L. Li, and T. Zhang. “Sparse Online Learning Via Truncated Gradient.” J. Mach. Learn. Res., Vol. 10, 2009, pp. 777–801.

[4] Nocedal, J. and S. J. Wright. Numerical Optimization, 2nd ed., New York: Springer, 2006.

[5] Shalev-Shwartz, S., Y. Singer, and N. Srebro. “Pegasos: Primal Estimated Sub-Gradient Solver for SVM.” Proceedings of the 24th International Conference on Machine Learning, ICML ’07, 2007, pp. 807–814.

[6] Wright, S. J., R. D. Nowak, and M. A. T. Figueiredo. “Sparse Reconstruction by Separable Approximation.” Trans. Sig. Proc., Vol. 57, No 7, 2009, pp. 2479–2493.

[7] Xiao, Lin. “Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization.” J. Mach. Learn. Res., Vol. 11, 2010, pp. 2543–2596.

[8] Xu, Wei. “Towards Optimal One Pass Large Scale Learning with Averaged Stochastic Gradient Descent.” CoRR, abs/1107.2490, 2011.