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fitrsvm trains or cross-validates a support vector machine (SVM) regression model on a low- through moderate-dimensional predictor data set. fitrsvm supports mapping the predictor data using kernel functions, and supports SMO, ISDA, or L1 soft-margin minimization via quadratic programming for objective-function minimization.

Mdl = fitrsvm(Tbl,ResponseVarName) returns a full, trained support vector machine (SVM) regression model Mdl trained using the predictors values in the table Tbl and the response values in Tbl.ResponseVarName.

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Mdl = fitrsvm(Tbl,formula) returns a full SVM regression model trained using the predictors values in the table Tbl. formula is an explanatory model of the response and a subset of predictor variables in Tbl used to fit Mdl.

Mdl = fitrsvm(___,Name,Value) returns an SVM regression model with additional options specified by one or more name-value pair arguments, using any of the previous syntaxes. For example, you can specify the kernel function or train a cross-validated model.

Create a model suitable for making predictions by passing the entire data set to fitrsvm, and specify all name-value pair arguments that yielded the better-performing model. However, do not specify any cross-validation options.

Sample data used to train the model, specified as a table. Eachrow of Tbl corresponds to one observation, andeach column corresponds to one predictor variable. Optionally, Tbl cancontain one additional column for the response variable. Multicolumnvariables and cell arrays other than cell arrays of character vectorsare not allowed.

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.

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.

Expected proportion of outliers in training data, specified as the comma-separated pair consisting of 'OutlierFraction' and a numeric scalar in the interval [0,1). fitrsvm removes observations with large gradients, ensuring that fitrsvm removes the fraction of observations specified by OutlierFraction by the time convergence is reached. This name-value pair is only valid when 'Solver' is 'ISDA'.

If RemoveDuplicates is true, then fitrsvm replaces duplicate observations in the training data with a single observation of the same value. The weight of the single observation is equal to the sum of the weights of the corresponding removed duplicates (see Weights).

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 fitrsvm 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 weights variable, or any other variables that the function does not use.

By default, if the predictor data is in a table (Tbl), fitrsvm 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), fitrsvm 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, fitrsvm creates dummy variables using two different schemes, depending on whether a categorical variable is unordered or ordered. For an unordered categorical variable, fitrsvm creates one dummy variable for each level of the categorical variable. For an ordered categorical variable, fitrsvm creates one less dummy variable than the number of categories. For details, see Automatic Creation of Dummy Variables.

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

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).

Observation weights, specified as the comma-separated pair consisting of 'Weights' and a vector of numeric values. The size of Weights must equal the number of rows in X. fitrsvm normalizes the values of Weights to sum to 1.

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

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

Sparsity in support vectors is a desirable property of an SVM model. To decrease the number of support vectors, set the BoxConstraint name-value pair argument to a large value. This action also increases the training time.

If you expect many fewer support vectors than observations in the training set, then you can significantly speed up convergence by shrinking the active-set using the name-value pair argument 'ShrinkagePeriod'. It is good practice to use 'ShrinkagePeriod',1000.

However, to maintain the original data set during training, fitrsvm must temporarily store separate data sets: the original and one without the duplicate observations. Therefore, if you specify true for data sets containing few duplicates, then fitrsvm consumes close to double the memory of the original data.

NaN, , empty character vector (''), empty string (""), and values indicate missing data values. fitrsvm removes entire rows of data corresponding to a missing response. When normalizing weights, fitrsvm ignores any weight corresponding to an observation with at least one missing predictor. Consequently, observation box constraints might not equal BoxConstraint.

If you set 'Standardize',true and 'Weights', then fitrsvm standardizes the predictors using their corresponding weighted means and weighted standard deviations. That is, fitrsvm standardizes predictor j (xj) using

The PredictorNames property stores one element for each of the original predictor variable names. For example, assume that there are three predictors, one of which is a categorical variable with three levels. Then PredictorNames is a 1-by-3 cell array of character vectors containing the original names of the predictor variables.

The ExpandedPredictorNames property stores one element for each of the predictor variables, including the dummy variables. For example, assume that there are three predictors, one of which is a categorical variable with three levels. Then ExpandedPredictorNames is a 1-by-5 cell array of character vectors containing the names of the predictor variables and the new dummy variables.

The SupportVectors property stores the predictor values for the support vectors, including the dummy variables. For example, assume that there are m support vectors and three predictors, one of which is a categorical variable with three levels. Then SupportVectors is an m-by-5 matrix. ff782bc1db