CompactClassificationSVM

Compact support vector machine (SVM) for one-class and binary classification

Description

CompactClassificationSVM is a compact version of the support vector machine (SVM) classifier. The compact classifier does not include the data used for training the SVM classifier. Therefore, you cannot perform some tasks, such as cross-validation, using the compact classifier. Use a compact SVM classifier for tasks such as predicting the labels of new data.

Creation

Create a CompactClassificationSVM model from a full, trained ClassificationSVM classifier by using compact.

Properties

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SVM Properties

Trained classifier coefficients, specified as an s-by-1 numeric vector. s is the number of support vectors in the trained classifier, sum(Mdl.IsSupportVector).

Alpha contains the trained classifier coefficients from the dual problem, that is, the estimated Lagrange multipliers. If you remove duplicates by using the RemoveDuplicates name-value pair argument of fitcsvm, then for a given set of duplicate observations that are support vectors, Alpha contains one coefficient corresponding to the entire set. That is, MATLAB® attributes a nonzero coefficient to one observation from the set of duplicates and a coefficient of 0 to all other duplicate observations in the set.

Data Types: single | double

Linear predictor coefficients, specified as a numeric vector. The length of Beta is equal to the number of predictors used to train the model.

MATLAB expands categorical variables in the predictor data using full dummy encoding. That is, MATLAB creates one dummy variable for each level of each categorical variable. Beta stores one value for each predictor variable, including the dummy variables. For example, if there are three predictors, one of which is a categorical variable with three levels, then Beta is a numeric vector containing five values.

If KernelParameters.Function is 'linear', then the classification score for the observation x is

$f\left(x\right)=\left(x/s\right)\prime \beta +b.$

Mdl stores β, b, and s in the properties Beta, Bias, and KernelParameters.Scale, respectively.

To estimate classification scores manually, you must first apply any transformations to the predictor data that were applied during training. Specifically, if you specify 'Standardize',true when using fitcsvm, then you must standardize the predictor data manually by using the mean Mdl.Mu and standard deviation Mdl.Sigma, and then divide the result by the kernel scale in Mdl.KernelParameters.Scale.

All SVM functions, such as resubPredict and predict, apply any required transformation before estimation.

If KernelParameters.Function is not 'linear', then Beta is empty ([]).

Data Types: single | double

Bias term, specified as a scalar.

Data Types: single | double

Kernel parameters, specified as a structure array. The kernel parameters property contains the fields listed in this table.

FieldDescription
Function

Kernel function used to compute the elements of the Gram matrix. For details, see 'KernelFunction'.

Scale

Kernel scale parameter used to scale all elements of the predictor data on which the model is trained. For details, see 'KernelScale'.

To display the values of KernelParameters, use dot notation. For example, Mdl.KernelParameters.Scale displays the kernel scale parameter value.

The software accepts KernelParameters as inputs and does not modify them.

Data Types: struct

Support vector class labels, specified as an s-by-1 numeric vector. s is the number of support vectors in the trained classifier, sum(Mdl.IsSupportVector).

A value of +1 in SupportVectorLabels indicates that the corresponding support vector is in the positive class (ClassNames{2}). A value of –1 indicates that the corresponding support vector is in the negative class (ClassNames{1}).

If you remove duplicates by using the RemoveDuplicates name-value pair argument of fitcsvm, then for a given set of duplicate observations that are support vectors, SupportVectorLabels contains one unique support vector label.

Data Types: single | double

Support vectors in the trained classifier, specified as an s-by-p numeric matrix. s is the number of support vectors in the trained classifier, sum(Mdl.IsSupportVector), and p is the number of predictor variables in the predictor data.

SupportVectors contains rows of the predictor data X that MATLAB considers to be support vectors. If you specify 'Standardize',true when training the SVM classifier using fitcsvm, then SupportVectors contains the standardized rows of X.

If you remove duplicates by using the RemoveDuplicates name-value pair argument of fitcsvm, then for a given set of duplicate observations that are support vectors, SupportVectors contains one unique support vector.

Data Types: single | double

Other Classification Properties

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: double

Unique class labels used in training, specified as a categorical or character array, logical or numeric vector, or cell array of character vectors. ClassNames has the same data type as the class labels Y. (The software treats string arrays as cell arrays of character vectors.) ClassNames also determines the class order.

Data Types: single | double | logical | char | cell | categorical

Misclassification cost, specified as a numeric square matrix, where Cost(i,j) is the cost of classifying a point into class j if its true class is i.

During training, the software updates the prior probabilities by incorporating the penalties described in the cost matrix.

• For two-class learning, Cost always has this form: Cost(i,j) = 1 if i ~= j, and Cost(i,j) = 0 if i = j. The rows correspond to the true class and the columns correspond to the predicted class. The order of the rows and columns of Cost corresponds to the order of the classes in ClassNames.

• For one-class learning, Cost = 0.

For more details, see Algorithms.

Data Types: double

Expanded predictor names, specified as a cell array of character vectors.

If the model uses dummy variable encoding for categorical variables, then ExpandedPredictorNames includes the names that describe the expanded variables. Otherwise, ExpandedPredictorNames is the same as PredictorNames.

Data Types: cell

Predictor means, specified as a numeric vector. If you specify 'Standardize',1 or 'Standardize',true when you train an SVM classifier using fitcsvm, then the length of Mu is equal to the number of predictors.

MATLAB expands categorical variables in the predictor data using full dummy encoding. That is, MATLAB creates one dummy variable for each level of each categorical variable. Mu stores one value for each predictor variable, including the dummy variables. However, MATLAB does not standardize the columns that contain categorical variables.

If you set 'Standardize',false when you train the SVM classifier using fitcsvm, then Mu is an empty vector ([]).

Data Types: single | double

Predictor variable names, specified as a cell array of character vectors. The order of the elements of PredictorNames corresponds to the order in which the predictor names appear in the training data.

Data Types: cell

Prior probabilities for each class, specified as a numeric vector. The order of the elements of Prior corresponds to the elements of Mdl.ClassNames.

For two-class learning, if you specify a cost matrix, then the software updates the prior probabilities by incorporating the penalties described in the cost matrix.

For more details, see Algorithms.

Data Types: single | double

Score transformation, specified as a character vector or function handle. ScoreTransform represents a built-in transformation function or a function handle for transforming predicted classification scores.

To change the score transformation function to function, for example, use dot notation.

• For a built-in function, enter a character vector.

Mdl.ScoreTransform = 'function';

This table describes the available built-in functions.

ValueDescription
'doublelogit'1/(1 + e–2x)
'invlogit'log(x / (1 – x))
'ismax'Sets the score for the class with the largest score to 1, and sets the scores for all other classes to 0
'logit'1/(1 + ex)
'none' or 'identity'x (no transformation)
'sign'–1 for x < 0
0 for x = 0
1 for x > 0
'symmetric'2x – 1
'symmetricismax'Sets the score for the class with the largest score to 1, and sets the scores for all other classes to –1
'symmetriclogit'2/(1 + ex) – 1

• For a MATLAB function or a function that you define, enter its function handle.

Mdl.ScoreTransform = @function;

function must accept a matrix (the original scores) and return a matrix of the same size (the transformed scores).

Data Types: char | function_handle

Predictor standard deviations, specified as a numeric vector.

If you specify 'Standardize',true when you train the SVM classifier using fitcsvm, then the length of Sigma is equal to the number of predictor variables.

MATLAB expands categorical variables in the predictor data using full dummy encoding. That is, MATLAB creates one dummy variable for each level of each categorical variable. Sigma stores one value for each predictor variable, including the dummy variables. However, MATLAB does not standardize the columns that contain categorical variables.

If you set 'Standardize',false when you train the SVM classifier using fitcsvm, then Sigma is an empty vector ([]).

Data Types: single | double

Object Functions

 compareHoldout Compare accuracies of two classification models using new data discardSupportVectors Discard support vectors for linear support vector machine (SVM) classifier edge Find classification edge for support vector machine (SVM) classifier fitPosterior Fit posterior probabilities for compact support vector machine (SVM) classifier incrementalLearner Convert binary classification support vector machine (SVM) model to incremental learner lime Local interpretable model-agnostic explanations (LIME) loss Find classification error for support vector machine (SVM) classifier margin Find classification margins for support vector machine (SVM) classifier partialDependence Compute partial dependence plotPartialDependence Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots predict Classify observations using support vector machine (SVM) classifier shapley Shapley values update Update model parameters for code generation

Examples

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Reduce the size of a full support vector machine (SVM) classifier by removing the training data. Full SVM classifiers (that is, ClassificationSVM classifiers) hold the training data. To improve efficiency, use a smaller classifier.

Train an SVM classifier. Standardize the predictor data and specify the order of the classes.

SVMModel = fitcsvm(X,Y,'Standardize',true,...
'ClassNames',{'b','g'})
SVMModel =
ClassificationSVM
ResponseName: 'Y'
CategoricalPredictors: []
ClassNames: {'b'  'g'}
ScoreTransform: 'none'
NumObservations: 351
Alpha: [90x1 double]
Bias: -0.1342
KernelParameters: [1x1 struct]
Mu: [0.8917 0 0.6413 0.0444 0.6011 0.1159 0.5501 ... ]
Sigma: [0.3112 0 0.4977 0.4414 0.5199 0.4608 0.4927 ... ]
BoxConstraints: [351x1 double]
ConvergenceInfo: [1x1 struct]
IsSupportVector: [351x1 logical]
Solver: 'SMO'

Properties, Methods

SVMModel is a ClassificationSVM classifier.

Reduce the size of the SVM classifier.

CompactSVMModel = compact(SVMModel)
CompactSVMModel =
CompactClassificationSVM
ResponseName: 'Y'
CategoricalPredictors: []
ClassNames: {'b'  'g'}
ScoreTransform: 'none'
Alpha: [90x1 double]
Bias: -0.1342
KernelParameters: [1x1 struct]
Mu: [0.8917 0 0.6413 0.0444 0.6011 0.1159 0.5501 ... ]
Sigma: [0.3112 0 0.4977 0.4414 0.5199 0.4608 0.4927 ... ]
SupportVectors: [90x34 double]
SupportVectorLabels: [90x1 double]

Properties, Methods

CompactSVMModel is a CompactClassificationSVM classifier.

Display the amount of memory used by each classifier.

whos('SVMModel','CompactSVMModel')
Name                 Size             Bytes  Class                                                 Attributes

CompactSVMModel      1x1              31058  classreg.learning.classif.CompactClassificationSVM
SVMModel             1x1             141148  ClassificationSVM

The full SVM classifier (SVMModel) is more than four times larger than the compact SVM classifier (CompactSVMModel).

To label new observations efficiently, you can remove SVMModel from the MATLAB® Workspace, and then pass CompactSVMModel and new predictor values to predict.

To further reduce the size of the compact SVM classifier, use the discardSupportVectors function to discard support vectors.

Train and cross-validate an SVM classifier. Standardize the predictor data and specify the order of the classes.

rng(1);  % For reproducibility
CVSVMModel = fitcsvm(X,Y,'Standardize',true,...
'ClassNames',{'b','g'},'CrossVal','on')
CVSVMModel =
ClassificationPartitionedModel
CrossValidatedModel: 'SVM'
PredictorNames: {1x34 cell}
ResponseName: 'Y'
NumObservations: 351
KFold: 10
Partition: [1x1 cvpartition]
ClassNames: {'b'  'g'}
ScoreTransform: 'none'

Properties, Methods

CVSVMModel is a ClassificationPartitionedModel cross-validated SVM classifier. By default, the software implements 10-fold cross-validation.

Alternatively, you can cross-validate a trained ClassificationSVM classifier by passing it to crossval.

Inspect one of the trained folds using dot notation.

CVSVMModel.Trained{1}
ans =
CompactClassificationSVM
ResponseName: 'Y'
CategoricalPredictors: []
ClassNames: {'b'  'g'}
ScoreTransform: 'none'
Alpha: [78x1 double]
Bias: -0.2209
KernelParameters: [1x1 struct]
Mu: [0.8888 0 0.6320 0.0406 0.5931 0.1205 0.5361 ... ]
Sigma: [0.3149 0 0.5033 0.4441 0.5255 0.4663 0.4987 ... ]
SupportVectors: [78x34 double]
SupportVectorLabels: [78x1 double]

Properties, Methods

Each fold is a CompactClassificationSVM classifier trained on 90% of the data.

Estimate the generalization error.

genError = kfoldLoss(CVSVMModel)
genError = 0.1168

On average, the generalization error is approximately 12%.

 Hastie, T., R. Tibshirani, and J. Friedman. The Elements of Statistical Learning, Second Edition. NY: Springer, 2008.

 Scholkopf, B., J. C. Platt, J. C. Shawe-Taylor, A. J. Smola, and R. C. Williamson. “Estimating the Support of a High-Dimensional Distribution.” Neural Computation. Vol. 13, Number 7, 2001, pp. 1443–1471.

 Christianini, N., and J. C. Shawe-Taylor. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge, UK: Cambridge University Press, 2000.

 Scholkopf, B., and A. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond, Adaptive Computation and Machine Learning. Cambridge, MA: The MIT Press, 2002.