ClassificationLinear Class

Linear model for binary classification of high-dimensional data

Description

ClassificationLinear is a trained linear model object for binary classification; the linear model is a support vector machine (SVM) or logistic regression model. fitclinear fits a ClassificationLinear model by minimizing the objective function using techniques that reduce computation time for high-dimensional data sets (e.g., stochastic gradient descent). The classification loss plus the regularization term compose the objective function.

Unlike other classification models, and for economical memory usage, ClassificationLinear model objects do not store the training data. However, they do store, for example, the estimated linear model coefficients, prior-class probabilities, and the regularization strength.

You can use trained ClassificationLinear models to predict labels or classification scores for new data. For details, see predict.

Construction

Create a ClassificationLinear object by using fitclinear.

Properties

Linear Classification Properties

Lambda — Regularization term strength

nonnegative scalar | vector of nonnegative values

Regularization term strength, specified as a nonnegative scalar or vector of nonnegative values.

Data Types: double | single

Learner — Linear classification model type

'logistic' | 'svm'

Linear classification model type, specified as 'logistic' or 'svm'.

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.

Value	Algorithm	Loss Function	FittedLoss Value
'svm'	Support vector machine	Hinge: $$\ell \left[y,f\left(x\right)\right]=\max \left[0,1-yf\left(x\right)\right]$$	'hinge'
'logistic'	Logistic regression	Deviance (logistic): $$\ell \left[y,f\left(x\right)\right]=\log \left\{1+\exp \left[-yf\left(x\right)\right]\right\}$$	'logit'

Beta — Linear coefficient estimates

numeric vector

Linear coefficient estimates, specified as a numeric vector with length equal to the number of expanded predictors (see ExpandedPredictorNames).

Data Types: double

Bias — Estimated bias term

numeric scalar

Estimated bias term or model intercept, specified as a numeric scalar.

Data Types: double

FittedLoss — Loss function used to fit linear model

'hinge' | 'logit'

This property is read-only.

Loss function used to fit the linear model, specified as 'hinge' or 'logit'.

Value	Algorithm	Loss Function	Learner Value
'hinge'	Support vector machine	Hinge: $$\ell \left[y,f\left(x\right)\right]=\max \left[0,1-yf\left(x\right)\right]$$	'svm'
'logit'	Logistic regression	Deviance (logistic): $$\ell \left[y,f\left(x\right)\right]=\log \left\{1+\exp \left[-yf\left(x\right)\right]\right\}$$	'logistic'

Regularization — Complexity penalty type

'lasso (L1)' | 'ridge (L2)'

Complexity penalty type, specified as 'lasso (L1)' or 'ridge (L2)'.

The software composes the objective function for minimization from the sum of the average loss function (see FittedLoss) and a regularization value from this table.

Value	Description
'lasso (L1)'	Lasso (L1) penalty: $$\lambda {\displaystyle \sum _{j=1}^p\left|{\beta }_j\right|}$$
'ridge (L2)'	Ridge (L2) penalty: $$\frac{\lambda }{2}{\displaystyle \sum _{j=1}^p{\beta }_j^2}$$

λ specifies the regularization term strength (see Lambda).

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

Other Classification Properties

CategoricalPredictors — Categorical predictor indices

vector of positive integers | []

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

ClassNames — Unique class labels

categorical array | character array | logical vector | numeric vector | cell array of character vectors

This property is read-only.

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

Cost — Misclassification costs

square numeric matrix

This property is read-only.

Misclassification costs, specified as a square numeric matrix. Cost has K rows and columns, where K is the number of classes.

Cost(i,j) is the cost of classifying a point into class j if its true class is i. The order of the rows and columns of Cost corresponds to the order of the classes in ClassNames.

Data Types: double

ModelParameters — Parameters used for training model

structure

Parameters used for training the ClassificationLinear model, specified as a structure.

Access fields of ModelParameters using dot notation. For example, access the relative tolerance on the linear coefficients and the bias term by using Mdl.ModelParameters.BetaTolerance.

Data Types: struct

PredictorNames — Predictor names

cell array of character vectors

Predictor names in order of their appearance in the predictor data, specified as a cell array of character vectors. The length of PredictorNames is equal to the number of variables in the training data X or Tbl used as predictor variables.

Data Types: cell

ExpandedPredictorNames — Expanded predictor names

cell array of character vectors

Expanded predictor names, specified 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

Prior — Prior class probabilities

numeric vector

This property is read-only.

Prior class probabilities, specified as a numeric vector. Prior has as many elements as classes in ClassNames, and the order of the elements corresponds to the elements of ClassNames.

Data Types: double

ResponseName — Response variable name

character vector

Response variable name, specified as a character vector.

Data Types: char

ScoreTransform — Score transformation function

'doublelogit' | 'invlogit' | 'ismax' | 'logit' | 'none' | function handle | ...

Score transformation function to apply to predicted scores, specified as a function name or function handle.

For linear classification models and before transformation, the predicted classification score for the observation x (row vector) is f(x) = xβ + b, where β and b correspond to Mdl.Beta and Mdl.Bias, respectively.

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

• For a built-in function, enter this code and replace function with a value in the table.

  Mdl.ScoreTransform = 'function';

  Value	Description
  "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 + e–x)
  "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 + e–x) – 1

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

  Mdl.ScoreTransform = @function;

  function must accept a matrix of the original scores for each class, and then return a matrix of the same size representing the transformed scores for each class.

Data Types: char | function_handle

Object Functions

edge	Classification edge for linear classification models
incrementalLearner	Convert linear model for binary classification to incremental learner
lime	Local interpretable model-agnostic explanations (LIME)
loss	Classification loss for linear classification models
margin	Classification margins for linear classification models
partialDependence	Compute partial dependence
plotPartialDependence	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
predict	Predict labels for linear classification models
shapley	Shapley values
selectModels	Choose subset of regularized, binary linear classification models
update	Update model parameters for code generation

Copy Semantics

Value. To learn how value classes affect copy operations, see Copying Objects.

Examples

Train Linear Classification Model

Train a binary, linear classification model using support vector machines, dual SGD, and ridge regularization.

Load the NLP data set.

load nlpdata

X is a sparse matrix of predictor data, and Y is a categorical vector of class labels. There are more than two classes in the data.

Identify the labels that correspond to the Statistics and Machine Learning Toolbox™ documentation web pages.

Ystats = Y == 'stats';

Train a binary, linear classification model that can identify whether the word counts in a documentation web page are from the Statistics and Machine Learning Toolbox™ documentation. Train the model using the entire data set. Determine how well the optimization algorithm fit the model to the data by extracting a fit summary.

rng(1); % For reproducibility 
[Mdl,FitInfo] = fitclinear(X,Ystats)

Mdl = 
  ClassificationLinear
      ResponseName: 'Y'
        ClassNames: [0 1]
    ScoreTransform: 'none'
              Beta: [34023x1 double]
              Bias: -1.0059
            Lambda: 3.1674e-05
           Learner: 'svm'

FitInfo = struct with fields:
                    Lambda: 3.1674e-05
                 Objective: 5.3783e-04
                 PassLimit: 10
                 NumPasses: 10
                BatchLimit: []
             NumIterations: 238561
              GradientNorm: NaN
         GradientTolerance: 0
      RelativeChangeInBeta: 0.0562
             BetaTolerance: 1.0000e-04
             DeltaGradient: 1.4582
    DeltaGradientTolerance: 1
           TerminationCode: 0
         TerminationStatus: {'Iteration limit exceeded.'}
                     Alpha: [31572x1 double]
                   History: []
                   FitTime: 0.1010
                    Solver: {'dual'}

Mdl is a ClassificationLinear model. You can pass Mdl and the training or new data to loss to inspect the in-sample classification error. Or, you can pass Mdl and new predictor data to predict to predict class labels 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. Because training time is small, you can try to retrain the model, but increase the number of passes through the data. This can improve measures like DeltaGradient.

Predict Class Labels Using Linear Classification Model

Load the NLP data set.

load nlpdata
n = size(X,1); % Number of observations

Identify the labels that correspond to the Statistics and Machine Learning Toolbox™ documentation web pages.

Ystats = Y == 'stats';

Hold out 5% of the data.

rng(1); % For reproducibility
cvp = cvpartition(n,'Holdout',0.05)

cvp = 
Hold-out cross validation partition
   NumObservations: 31572
       NumTestSets: 1
         TrainSize: 29994
          TestSize: 1578
          IsCustom: 0

cvp is a CVPartition object that defines the random partition of n data into training and test sets.

Train a binary, linear classification model using the training set that can identify whether the word counts in a documentation web page are from the Statistics and Machine Learning Toolbox™ documentation. For faster training time, orient the predictor data matrix so that the observations are in columns.

idxTrain = training(cvp); % Extract training set indices
X = X';
Mdl = fitclinear(X(:,idxTrain),Ystats(idxTrain),'ObservationsIn','columns');

Predict observations and classification error for the hold out sample.

idxTest = test(cvp); % Extract test set indices
labels = predict(Mdl,X(:,idxTest),'ObservationsIn','columns');
L = loss(Mdl,X(:,idxTest),Ystats(idxTest),'ObservationsIn','columns')

L = 
7.1753e-04

Mdl misclassifies fewer than 1% of the out-of-sample observations.

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

• The predict and update functions support code generation.

• When you train a linear classification model by using fitclinear, the following restrictions apply.

  • If the predictor data input argument value is a matrix, it must be a full, numeric matrix. Code generation does not support sparse data.

  • You can specify only one regularization strength—'auto' or a nonnegative scalar for the 'Lambda' name-value pair argument.

  • The value of the 'ScoreTransform' name-value pair argument cannot be an anonymous function.

  • For code generation with a coder configurer, the following additional restrictions apply.

    • Categorical predictors (logical, categorical, char, string, or cell) are not supported. You cannot use the CategoricalPredictors name-value argument. To include categorical predictors in a model, preprocess them by using dummyvar before fitting the model.

    • Class labels with the categorical data type are not supported. Both the class label value in the training data (Tbl or Y) and the value of the ClassNames name-value argument cannot be an array with the categorical data type.

For more information, see Introduction to Code Generation.

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

• The following object functions fully support GPU arrays:

  • loss

  • selectModels

  • partialDependence

  • plotPartialDependence

  • predict

  • margin

  • edge

• 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.

Version History

Introduced in R2016a

R2024a: Specify GPU arrays for ClassificationLinear object functions (requires Parallel Computing Toolbox)

You can fit a ClassificationLinear object with GPU arrays by using fitclinear. Most ClassificationLinear 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, and update.

R2022a: Cost property stores the user-specified cost matrix

Starting in R2022a, the Cost property stores the user-specified cost matrix, so that you can compute the observed misclassification cost using the specified cost value. The software stores normalized prior probabilities (Prior) that do not reflect the penalties described in the cost matrix. To compute the observed misclassification cost, specify the LossFun name-value argument as "classifcost" when you call the loss function.

Note that model training has not changed and, therefore, the decision boundaries between classes have not changed.

For training, the fitting function updates the specified prior probabilities by incorporating the penalties described in the specified cost matrix, and then normalizes the prior probabilities and observation weights. This behavior has not changed. In previous releases, the software stored the default cost matrix in the Cost property and stored the prior probabilities used for training in the Prior property. Starting in R2022a, the software stores the user-specified cost matrix without modification, and stores normalized prior probabilities that do not reflect the cost penalties. For more details, see Misclassification Cost Matrix, Prior Probabilities, and Observation Weights.

Some object functions use the Cost and Prior properties:

• The loss function uses the cost matrix stored in the Cost property if you specify the LossFun name-value argument as "classifcost" or "mincost".

• The loss and edge functions use the prior probabilities stored in the Prior property to normalize the observation weights of the input data.

If you specify a nondefault cost matrix when you train a classification model, the object functions return a different value compared to previous releases.

If you want the software to handle the cost matrix, prior probabilities, and observation weights in the same way as in previous releases, adjust the prior probabilities and observation weights for the nondefault cost matrix, as described in Adjust Prior Probabilities and Observation Weights for Misclassification Cost Matrix. Then, when you train a classification model, specify the adjusted prior probabilities and observation weights by using the Prior and Weights name-value arguments, respectively, and use the default cost matrix.