CompactRegressionSVM - Compact support vector machine regression model - MATLAB (original) (raw)
Namespace: classreg.learning.regr
Compact support vector machine regression model
Description
CompactRegressionSVM is a compact support vector machine (SVM) regression model. It consumes less memory than a full, trained support vector machine model (RegressionSVM model) because it does not store the data used to train the model.
Because the compact model does not store the training data, you cannot use it to perform certain tasks, such as cross validation. However, you can use a compact SVM regression model to predict responses using new input data.
Construction
`compactMdl` = compact([mdl](#buwlkt7-1-mdl)) returns a compact SVM regression model compactMdl from a full, trained SVM regression model, mdl. For more information, see compact.
Input Arguments
Properties
Data Types: single | double
Primal linear problem coefficients, stored as a numeric vector of length p, where p is the number of predictors in the SVM regression model.
The values in Beta are the linear coefficients for the primal optimization problem.
If the model is obtained using a kernel function other than 'linear', this property is empty ('[]').
The predict method computes predicted response values for the model as YFIT = (X/S)×Beta + Bias, whereS is the value of the kernel scale stored in the KernelParameters.Scale property.
Data Types: double
Bias term in the SVM regression model, stored as a scalar value.
Data Types: double
This property is read-only.
Categorical predictor indices, specified as a vector of positive integers. CategoricalPredictors contains index values indicating that the corresponding predictors are categorical. The index values are between 1 and p, where p is the number of predictors used to train the model. If none of the predictors are categorical, then this property is empty ([]).
Data Types: single | double
Expanded predictor names, stored as a cell array of character vectors.
If the model uses encoding for categorical variables, then ExpandedPredictorNames includes the names that describe the expanded variables. Otherwise, ExpandedPredictorNames is the same as PredictorNames.
Data Types: cell
Kernel function parameters, stored as a structure with the following fields.
| Field | Description |
|---|---|
| Function | Kernel function name (a character vector). |
| Scale | Numeric scale factor used to divide predictor values. |
You can specify values for KernelParameters.Function and KernelParameters.Scale by using the KernelFunction and KernelScale name-value pair arguments in fitrsvm, respectively.
Data Types: struct
Predictor means, stored as a vector of numeric values.
If the training data is standardized, then Mu is a numeric vector of length p, where p is the number of predictors used to train the model. In this case, the predict method centers predictor matrix X by subtracting the corresponding element of Mu from each column.
If the training data is not standardized, then Mu is empty ('[]').
Data Types: single | double
Predictor names, stored as a cell array of character vectors containing the name of each predictor in the order in which they appear in X. PredictorNames has a length equal to the number of columns in X.
Data Types: cell
Response variable name, stored as a character vector.
Data Types: char
Data Types: char | string | function_handle
Predictor standard deviations, stored as a vector of numeric values.
If the training data is standardized, then Sigma is a numeric vector of length p, where p is the number of predictors used to train the model. In this case, the predict method scales the predictor matrix X by dividing each column by the corresponding element of Sigma, after centering each element using Mu.
If the training data is not standardized, then Sigma is empty ('[]').
Data Types: single | double
Data Types: single | double
Object Functions
| discardSupportVectors | Discard support vectors for linear support vector machine (SVM) regression model |
|---|---|
| gather | Gather properties of Statistics and Machine Learning Toolbox object from GPU |
| incrementalLearner | Convert support vector machine (SVM) regression model to incremental learner |
| lime | Local interpretable model-agnostic explanations (LIME) |
| loss | Regression error for support vector machine regression model |
| partialDependence | Compute partial dependence |
| plotPartialDependence | Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots |
| predict | Predict responses using support vector machine regression model |
| shapley | Shapley values |
| update | Update model parameters for code generation |
Copy Semantics
Value. To learn how value classes affect copy operations, see Copying Objects.
Examples
This example shows how to reduce the size of a full, trained SVM regression model by discarding the training data and some information related to the training process.
This example uses the abalone data from the UCI Machine Learning Repository. Download the data and save it in your current directory with the name 'abalone.data'. Read the data into a table.
tbl = readtable('abalone.data','Filetype','text','ReadVariableNames',false); rng default % for reproducibility
The sample data contains 4177 observations. All of the predictor variables are continuous except for sex, which is a categorical variable with possible values 'M' (for males), 'F' (for females), and 'I' (for infants). The goal is to predict the number of rings on the abalone, and thereby determine its age, using physical measurements.
Train an SVM regression model using a Gaussian kernel function and an automatic kernel scale. Standardize the data.
mdl = fitrsvm(tbl,'Var9','KernelFunction','gaussian','KernelScale','auto','Standardize',true)
mdl =
RegressionSVM PredictorNames: {1x8 cell} ResponseName: 'Var9' CategoricalPredictors: 1 ResponseTransform: 'none' Alpha: [3635x1 double] Bias: 10.8144 KernelParameters: [1x1 struct] Mu: [1x10 double] Sigma: [1x10 double] NumObservations: 4177 BoxConstraints: [4177x1 double] ConvergenceInfo: [1x1 struct] IsSupportVector: [4177x1 logical] Solver: 'SMO'
Properties, Methods
Compact the model.
compactMdl = compact(mdl)
compactMdl =
classreg.learning.regr.CompactRegressionSVM PredictorNames: {1x8 cell} ResponseName: 'Var9' CategoricalPredictors: 1 ResponseTransform: 'none' Alpha: [3635x1 double] Bias: 10.8144 KernelParameters: [1x1 struct] Mu: [1x10 double] Sigma: [1x10 double] SupportVectors: [3635x10 double]
Properties, Methods
The compacted model discards the training data and some information related to the training process.
Compare the size of the full model mdl and the compact model compactMdl.
vars = whos('compactMdl','mdl'); [vars(1).bytes,vars(2).bytes]
The compacted model consumes about half the memory of the full model.
References
[1] Nash, W.J., T. L. Sellers, S. R. Talbot, A. J. Cawthorn, and W. B. Ford. "The Population Biology of Abalone (Haliotis species) in Tasmania. I. Blacklip Abalone (H. rubra) from the North Coast and Islands of Bass Strait." Sea Fisheries Division, Technical Report No. 48, 1994.
[2] Waugh, S. "Extending and Benchmarking Cascade-Correlation: Extensions to the Cascade-Correlation Architecture and Benchmarking of Feed-forward Supervised Artificial Neural Networks." University of Tasmania Department of Computer Science thesis, 1995.
[3] Clark, D., Z. Schreter, A. Adams. "A Quantitative Comparison of Dystal and Backpropagation." submitted to the Australian Conference on Neural Networks, 1996.
[4] Lichman, M. UCI Machine Learning Repository, [http://archive.ics.uci.edu/ml\]. Irvine, CA: University of California, School of Information and Computer Science.
Extended Capabilities
Usage notes and limitations:
To integrate the prediction of an SVM regression model into Simulink®, you can use the RegressionSVM Predict block in the Statistics and Machine Learning Toolbox™ library or a MATLAB Function block with the
predictfunction.When you train an SVM regression model by using fitrsvm, the following restrictions apply.
- The value of the ResponseTransform name-value argument cannot be an anonymous function. For fixed-point code generation, the value must be
'none'(default). - For fixed-point code generation, the value of the KernelFunction name-value argument must be
'gaussian','linear', or'polynomial'. - Fixed-point code generation and code generation with a coder configurer do not support categorical predictors (
logical,categorical,char,string, orcell). You cannot use theCategoricalPredictorsname-value argument. To include categorical predictors in a model, preprocess them by using dummyvar before fitting the model.
- The value of the ResponseTransform name-value argument cannot be an anonymous function. For fixed-point code generation, the value must be
For more information, see Introduction to Code Generation.
Usage notes and limitations:
- The following object functions fully support GPU arrays:
- The object functions execute on a GPU if at least one of the following applies:
- The model was fitted with GPU arrays.
- The predictor data that you pass to the object function is a GPU array.
- The response data that you pass to the object function is a GPU array.
For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
Version History
Introduced in R2015b
Starting in R2023a, you can fit a CompactRegressionSVM object on a GPU by usingcompact. MostCompactRegressionSVM object functions now support GPU array input arguments so that they can execute on a GPU. The object functions that do not support GPU array inputs are incrementalLearner, lime, shapley, andupdate.