Statistics: CompactClassificationSVM

Class Definition: `CompactClassificationSVM`

statistics: CompactClassificationSVM

Compact Support Vector Machine classification

The CompactClassificationSVM class implements a compact version of a Support Vector Machine classifier object for one-class or two-class problems, which can predict responses for new data using the predict method.

A CompactClassificationSVM object is a compact version of a support vector machine model, ClassificationSVM. It does not include the training data resulting in a smaller classifier size, which can be used for making predictions from new data, but not for tasks such as cross validation. It can only be created from a ClassificationSVM model by using the compact object method.

See also: ClassificationSVM

Source Code: CompactClassificationSVM

Properties

A positive integer value specifying the number of predictors in the training dataset used for training the SVM model. This property is read-only.

A cell array of character vectors specifying the names of the predictor variables. The names are in the order in which the appear in the training dataset. This property is read-only.

A character vector specifying the name of the response variable Y. This property is read-only.

An array of unique values of the response variable Y, which has the same data types as the data in Y. This property is read-only. ClassNames can have any of the following datatypes:

Cell array of character vectors
Character array
Logical vector
Numeric vector

Specified as a function handle for transforming the classification scores. Add or change the ScoreTransform property using dot notation as in:

obj.ScoreTransform = 'function_name'
obj.ScoreTransform = @function_handle

When specified as a character vector, it can be any of the following built-in functions. Nevertherless, the ScoreTransform property always stores their function handle equivalent.

`Value`		`Description`
`"doublelogit"`		$1 ./ (1 + e×p .^ (-2××))$
`"invlogit"`		$log (× ./ (1 -×))$
`"ismax"`		Sets the score for the class with the largest score to 1, and for all other classes to 0
`"logit"`		$1 ./ (1 + e×p .^ (-×))$
`"none"`		$×$ (no transformation)
`"identity"`		$×$ (no transformation)
`"sign"`		$-1 for× < 0, 0 for× = 0, 1 for× > 0$
`"symmetric"`		$2×× + 1$
`"symmetricismax"`		Sets the score for the class with the largest score to 1, and for all other classes to -1
`"symmetriclogit"`		$2 ./ (1 + e×p .^ (-×)) - 1$

A boolean flag indicating whether the data in X have been standardized prior to training. This property is read-only.

A numeric vector of the same length as the columns in X containing the standard deviations of predictor variables. If the predictor variables have not been standardized, then Sigma is empty. This property is read-only.

A numeric vector of the same length as the columns in X containing the means of predictor variables. If the predictor variables have not been standardized, then Mu is empty. This property is read-only.

A structure containing the parameters used to train the SVM model with the following fields: SVMtype, BoxConstraint, CacheSize, KernelScale, KernelOffset, KernelFunction, PolynomialOrder, Nu, Tolerance, and Shrinking. This property is read-only.

A structure containing the trained model in 'libsvm' format. This property is read-only.

The coefficients of the trained SVM classifier specified as an $s×1$ numeric vector, where $s$ is the number of support vectors equal to sum (obj.IsSupportVector). If the SVM classifier was trained with a kernel function other than 'linear', then Alpha is empty. This property is read-only.

The linear predictor coefficients specified as an $s×1$ numeric vector, where $s$ is the number of support vectors equal to sum (obj.IsSupportVector). If the SVM classifier was trained with a 'linear' kernel function, then Beta is empty. This property is read-only.

The bias term specified as a scalar. This property is read-only.

An $N×1$ logical vector that flags whether a corresponding observation in the predictor data matrix is a Support Vector. $N$ is the number of observations in the training data. This property is read-only.

The support vector class labels specified as an $s×1$ numeric vector, where $s$ is the number of support vectors equal to sum (obj.IsSupportVector). A value of +1 in SupportVectorLabels indicates that the corresponding support vector belongs to the positive class (ClassNames{2}). A value of -1 indicates that the corresponding support vector belongs to the negative class (ClassNames{1}). This property is read-only.

The support vectors of the trained SVM classifier specified an $s×p$ numeric matrix, where $s$ is the number of support vectors equal to sum (obj.IsSupportVector), and $p$ is the number of predictor variables in the predictor data. This property is read-only.

Methods

CompactClassificationSVM: label = predict (obj, XC)
CompactClassificationSVM: [label, score] = predict (obj, XC)

label = predict (obj, XC) returns the vector of labels predicted for the corresponding instances in XC, using the predictor data in the CompactClassificationSVM model, obj. For one-class SVM model, +1 or -1 is returned.

obj must be a CompactClassificationSVM class object.
XC must be an $M×P$ numeric matrix with the same number of features $P$ as the corresponding predictors of the SVM model in obj.

[label, score] = predict (obj, XC) also returns score, which contains the decision values for each each prediction. Alternatively, score can contain the posterior probabilities if the ScoreTransform has been previously set using the fitPosterior method.

CompactClassificationSVM: m = margin (obj, X, Y)

m = margin (obj, X, Y) returns the classification margins for obj with data X and classification Y. m is a numeric vector of length size (X,1).

obj is a CompactClassificationSVM object.
X must be a $N×P$ numeric matrix of input data where rows correspond to observations and columns correspond to features or variables.
Y is $N×1$ matrix or cell matrix containing the class labels of corresponding predictor data in X. Y must have same numbers of Rows as X.

The classification margin for each observation is the difference between the classification score for the true class and the maximal classification score for the false classes.

See also: CompactClassificationSVM

CompactClassificationSVM: L = loss (obj, X, Y)
CompactClassificationSVM: L = loss (…, name, value)

L = loss (obj, X, Y) computes the loss, L, using the default loss function 'classiferror'.

obj is a CompactClassificationSVM object.
X must be a $N×P$ numeric matrix of input data where rows correspond to observations and columns correspond to features or variables.
Y is $N×1$ matrix or cell matrix containing the class labels of corresponding predictor data in X. Y must have same numbers of Rows as X.

L = loss (…, name, value) allows additional options specified by name-value pairs:

`Name`		`Value`
`"LossFun"`		Specifies the loss function to use. Can be a function handle with four input arguments (C, S, W, Cost) which returns a scalar value or one of: ’binodeviance’, ’classifcost’, ’classiferror’, ’exponential’, ’hinge’, ’logit’,’mincost’, ’quadratic’. `C` is a logical matrix of size $N×K$ , where $N$ is the number of observations and $K$ is the number of classes. The element `C(i,j)` is true if the class label of the i-th observation is equal to the j-th class. `S` is a numeric matrix of size $N×K$ , where each element represents the classification score for the corresponding class. `W` is a numeric vector of length $N$ , representing the observation weights. `Cost` is a $K×K$ matrix representing the misclassification costs.
`"Weights"`		Specifies observation weights, must be a numeric vector of length equal to the number of rows in X. Default is `ones (size (X, 1))`. loss normalizes the weights so that observation weights in each class sum to the prior probability of that class. When you supply Weights, loss computes the weighted classification loss.

See also: CompactClassificationSVM

CompactClassificationSVM: savemodel (obj, filename)

savemodel (obj, filename) saves each property of a CompactClassificationSVM object into an Octave binary file, the name of which is specified in filename, along with an extra variable, which defines the type classification object these variables constitute. Use loadmodel in order to load a classification object into Octave’s workspace.

Examples

 # and compare their size

 load fisheriris
 X = meas;
 Y = species;

 selected_classes = unique (Y)(randperm (3, 2));
 selected_indices = ismember (Y, selected_classes);
 X_selected = X(selected_indices, :);
 Y_selected = Y(selected_indices);
 Mdl = fitcsvm (X_selected, Y_selected, 'ClassNames', selected_classes);
 CMdl = crossval (Mdl)

CMdl =

  ClassificationPartitionedModel object with properties:

                   BinEdges: []
      CategoricalPredictors: []
                          X: [5.1000, 3.5000, 1.4000, 0.2000; 4.9000, 3, 1.4000, 0.2000; 4.7000, 3.2000, ...]
                          Y: [100x1 cell]
                 ClassNames: [2x1 cell]
                       Cost: []
        CrossValidatedModel: 'ClassificationSVM'
                      KFold: 10
            ModelParameters: [1x1 struct]
            NumObservations: 100
                  Partition: [1x1 cvpartition]
             PredictorNames: [1x4 cell]
                      Prior: []
               ResponseName: "Y"
             ScoreTransform: [1x1 function_handle]
                Standardize: 0
                    Trained: [10x1 cell]

Categories &

Functions List

Clustering

Clustering

Classification Classes

Classification Classes

Clustering Classes

Clustering Classes

Regression Classes

Regression Classes

Data Manipulation

Data Manipulation

Descriptive Statistics

Descriptive Statistics

Distribution Classes

Distribution Classes

Distribution Fitting

Distribution Fitting

Distribution Functions

Distribution Functions

Distribution Statistics

Distribution Statistics

Distribution Wrappers

Distribution Wrappers

Experimental Design

Experimental Design

Machine Learning

Machine Learning

Model Fitting

Model Fitting

Hypothesis Testing

Hypothesis Testing

I/O

I/O

Plotting

Plotting

Regression

Regression

Transforms

Transforms

Class Definition: CompactClassificationSVM

statistics: CompactClassificationSVM

Properties

NumPredictors

PredictorNames

ResponseName

ClassNames

ScoreTransform

Standardize

Sigma

Mu

ModelParameters

Model

Alpha

Beta

Bias

IsSupportVector

SupportVectorLabels

SupportVectors

Methods

predict

CompactClassificationSVM: label = predict (obj, XC)

CompactClassificationSVM: [label, score] = predict (obj, XC)

margin

CompactClassificationSVM: m = margin (obj, X, Y)

loss

CompactClassificationSVM: L = loss (obj, X, Y)

CompactClassificationSVM: L = loss (…, name, value)

savemodel

CompactClassificationSVM: savemodel (obj, filename)

Examples

Class Definition: `CompactClassificationSVM`

`NumPredictors`

`PredictorNames`

`ResponseName`

`ClassNames`

`ScoreTransform`

`Standardize`

`Sigma`

`Mu`

`ModelParameters`

`Model`

`Alpha`

`Beta`

`Bias`

`IsSupportVector`

`SupportVectorLabels`

`SupportVectors`

`predict`

`CompactClassificationSVM: label = predict (obj, XC)`

`CompactClassificationSVM: [label, score] = predict (obj, XC)`

`margin`

`CompactClassificationSVM: m = margin (obj, X, Y)`

`loss`

`CompactClassificationSVM: L = loss (obj, X, Y)`

`CompactClassificationSVM: L = loss (…, name, value)`

`savemodel`

`CompactClassificationSVM: savemodel (obj, filename)`