incrementalClassificationLinear

Binary classification linear model for incremental learning

Description

incrementalClassificationLinear creates an incrementalClassificationLinear model object, which represents a binary classification linear model for incremental learning. Supported learners include support vector machine (SVM) and logistic regression.

Unlike other Statistics and Machine Learning Toolbox™ model objects, incrementalClassificationLinear can be called directly. Also, you can specify learning options, such as performance metrics configurations, parameter values, and the objective solver, before fitting the model to data. After you create an incrementalClassificationLinear object, it is prepared for incremental learning.

incrementalClassificationLinear is best suited for incremental learning. For a traditional approach to training an SVM or linear model for binary classification (such as creating a model by fitting it to data, performing cross-validation, tuning hyperparameters, and so on), see fitcsvm or fitclinear. For multiclass incremental learning, see incrementalClassificationECOC and incrementalClassificationNaiveBayes.

Creation

You can create an incrementalClassificationLinear model object in several ways:

• Call the function directly — Configure incremental learning options, or specify initial values for linear model parameters and hyperparameters, by calling incrementalClassificationLinear directly. This approach is best when you do not have data yet or you want to start incremental learning immediately.

• Convert a traditionally trained model — To initialize a binary classification linear model for incremental learning using the model coefficients and hyperparameters of a trained model object, you can convert the traditionally trained model to an incrementalClassificationLinear model object by passing it to the incrementalLearner function. This table contains links to the appropriate reference pages.

  Convertible Model Object	Conversion Function
  ClassificationSVM or CompactClassificationSVM	incrementalLearner
  ClassificationLinear	incrementalLearner

• Convert a template object — You can convert the template object to an incrementalClassificationLinear model object by passing it to the incrementalLearner function. This table contains links to the appropriate reference pages.

  Convertible Template Object	Conversion Function
  templateSVM	incrementalLearner
  templateLinear	incrementalLearner

• Call an incremental learning function — fit, updateMetrics, and updateMetricsAndFit accept a configured incrementalClassificationLinear model object and data as input, and return an incrementalClassificationLinear model object updated with information learned from the input model and data.

Syntax

Mdl = incrementalClassificationLinear()

Mdl = incrementalClassificationLinear(Name=Value)

Description

Mdl = incrementalClassificationLinear() returns a default incremental learning model object for binary linear classification, Mdl. Properties of a default model contain placeholders for unknown model parameters. You must train a default model before you can track its performance or generate predictions from it.

Mdl = incrementalClassificationLinear(Name=Value) sets properties and additional options using name-value arguments. Enclose each name in quotes. For example, incrementalClassificationLinear(Beta=[0.1 0.3],Bias=1,MetricsWarmupPeriod=100) sets the vector of linear model coefficients β to [0.1 0.3], the bias β₀ to 1, and the metrics warm-up period to 100.

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Example: Standardize=true standardizes the predictor data using the predictor means and standard deviations estimated during the estimation period.

Metrics — Model performance metrics to track during incremental learning

"classiferror" (default) | string vector | function handle | cell vector | structure array | "binodeviance" | "exponential" | "hinge" | "logit" | "quadratic"

Model performance metrics to track during incremental learning, specified as a built-in loss function name, string vector of names, function handle (@metricName), structure array of function handles, or cell vector of names, function handles, or structure arrays.

When Mdl is warm (see IsWarm), updateMetrics and updateMetricsAndFit track performance metrics in the Metrics property of Mdl.

The following table lists the built-in loss function names. You can specify more than one by using a string vector.

Name	Description
"binodeviance"	Binomial deviance
"classiferror"	Classification error
"exponential"	Exponential
"hinge"	Hinge
"logit"	Logistic
"quadratic"	Quadratic

For more details on the built-in loss functions, see loss.

Example: 'Metrics',["classiferror" "hinge"]

To specify a custom function that returns a performance metric, use function handle notation. The function must have this form:

metric = customMetric(C,S)

• The output argument metric is an n-by-1 numeric vector, where each element is the loss of the corresponding observation in the data processed by the incremental learning functions during a learning cycle.

• You specify the function name (customMetric).

• C is an n-by-2 logical matrix with rows indicating the class to which the corresponding observation belongs. The column order corresponds to the class order in the ClassNames property. Create C by setting C(p,q) = 1, if observation p is in class q, for each observation in the specified data. Set the other element in row p to 0.

• S is an n-by-2 numeric matrix of predicted classification scores. S is similar to the score output of predict, where rows correspond to observations in the data and the column order corresponds to the class order in the ClassNames property. S(p,q) is the classification score of observation p being classified in class q.

To specify multiple custom metrics and assign a custom name to each, use a structure array. To specify a combination of built-in and custom metrics, use a cell vector.

Example: 'Metrics',struct('Metric1',@customMetric1,'Metric2',@customMetric2)

Example: 'Metrics',{@customMetric1 @customMetric2 'logit' struct('Metric3',@customMetric3)}

updateMetrics and updateMetricsAndFit store specified metrics in a table in the Metrics property. The data type of Metrics determines the row names of the table.

'Metrics' Value Data Type	Description of Metrics Property Row Name	Example
String or character vector	Name of corresponding built-in metric	Row name for "classiferror" is "ClassificationError"
Structure array	Field name	Row name for struct('Metric1',@customMetric1) is "Metric1"
Function handle to function stored in a program file	Name of function	Row name for @customMetric is "customMetric"
Anonymous function	CustomMetric_j, where j is metric j in Metrics	Row name for @(C,S)customMetric(C,S)... is CustomMetric_1

For more details on performance metrics options, see Performance Metrics.

Data Types: char | string | struct | cell | function_handle

Standardize — Flag to standardize predictor data

'auto' (default) | false | true

Flag to standardize the predictor data, specified as a value in this table.

Value	Description
'auto'	incrementalClassificationLinear determines whether the predictor variables need to be standardized. See Standardize Data.
true	The software standardizes the predictor data. For more details, see Standardize Data.
false	The software does not standardize the predictor data.

Example: 'Standardize',true

Data Types: logical | char | string

Shuffle — Flag for shuffling observations

true (default) | false

Flag for shuffling the observations at each iteration, specified as a value in this table.

Value	Description
true	The software shuffles the observations in an incoming chunk of data before the fit function fits the model. This action reduces bias induced by the sampling scheme.
false	The software processes the data in the order received.

This option is valid only when Solver is 'scale-invariant'. When Solver is 'sgd' or 'asgd', the software always shuffles the observations in an incoming chunk of data before processing the data.

Example: 'Shuffle',false

Data Types: logical

Properties

You can set most properties by using name-value argument syntax only when you call incrementalClassificationLinear directly. You can set some properties when you call incrementalLearner to convert a traditionally trained model object or model template object. You cannot set the properties FittedLoss, NumTrainingObservations, Mu, Sigma, SolverOptions, and IsWarm.

Classification Model Parameters

Beta — Linear model coefficients β

Read-only: numeric vector

This property is read-only.

Linear model coefficients β, specified as a NumPredictors-by-1 numeric vector.

Incremental fitting functions estimate Beta during training. The default initial Beta value depends on how you create the model:

• If you convert a traditionally trained model object or template model object to create Mdl, the initial value is specified by the corresponding property of the object.

• Otherwise, the initial value is zeros(NumPredictors,1).

Data Types: single | double

Bias — Model intercept β₀

Read-only: numeric scalar

This property is read-only.

Model intercept β₀, or bias term, specified as a numeric scalar.

Incremental fitting functions estimate Bias during training. The default initial Bias value depends on how you create the model:

• If you convert a traditionally trained model object or template model object to create Mdl, the initial value is specified by the corresponding property of the object.

• Otherwise, the initial value is 0.

Data Types: single | double

ClassNames — Unique class labels

Read-only: categorical array | character array | string array | logical vector | numeric vector | cell array of character vectors

This property is read-only.

Unique class labels used in training the model, specified as a categorical, character, or string array, logical or numeric vector, or cell array of character vectors. ClassNames and the response data must have the same data type. (The software treats string arrays as cell arrays of character vectors.)

The default ClassNames value depends on how you create the model:

• If you convert a traditionally trained model to create Mdl, ClassNames is specified by the corresponding property of the traditionally trained model.

• Otherwise, incremental fitting functions infer ClassNames during training.

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

FittedLoss — Loss function used to fit linear model

Read-only: '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'

Learner — Linear classification model type

Read-only: 'svm' | 'logistic'

This property is read-only.

Linear classification model type, specified as 'svm' or 'logistic'. incrementalClassificationLinear stores the Learner value as a character vector.

In the following 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'

The default Learner value depends on how you create the model:

• If you convert a traditionally trained SVM classification model object (ClassificationSVM or CompactClassificationSVM) or SVM model template object (returned by templateSVM) to create Mdl, Learner is 'svm'.

• If you convert a traditionally trained linear classification model object (ClassificationLinear) or linear classification model template object (returned by templateLinear) to create Mdl, Learner is specified by the corresponding property of the object.

• Otherwise, the default value is 'svm'.

Data Types: char | string

NumPredictors — Number of predictor variables

Read-only: nonnegative numeric scalar

This property is read-only.

Number of predictor variables, specified as a nonnegative numeric scalar.

The default NumPredictors value depends on how you create the model:

• If you convert a traditionally trained model to create Mdl, NumPredictors is specified by the corresponding property of the traditionally trained model.

• If you create Mdl by calling incrementalClassificationLinear directly, you can specify NumPredictors by using name-value argument syntax.

• Otherwise, the default value is 0, and incremental fitting functions infer NumPredictors from the predictor data during training.

Data Types: double

NumTrainingObservations — Number of observations fit to incremental model

Read-only: 0 (default) | nonnegative numeric scalar

This property is read-only.

Number of observations fit to the incremental model Mdl, specified as a nonnegative numeric scalar. NumTrainingObservations increases when you pass Mdl and training data to fit or updateMetricsAndFit.

Note

If you convert a traditionally trained model to create Mdl, incrementalClassificationLinear does not add the number of observations fit to the traditionally trained model to NumTrainingObservations.

Data Types: double

Prior — Prior class probabilities

Read-only: 'empirical' | 'uniform' | numeric vector

This property is read-only.

Prior class probabilities, specified as 'empirical', 'uniform', or a numeric vector. incrementalClassificationLinear stores the Prior value as a numeric vector.

Value	Description
'empirical'	Incremental learning functions infer prior class probabilities from the observed class relative frequencies in the response data during incremental training (after the estimation period EstimationPeriod).
'uniform'	For each class, the prior probability is 1/2.
numeric vector	Custom, normalized prior probabilities. The order of the elements of Prior corresponds to the elements of the ClassNames property.

The default Prior value depends on how you create the model:

• If you convert a traditionally trained model to create Mdl, Prior is specified by the corresponding property of the traditionally trained model.

• Otherwise, the default value is 'empirical'.

Data Types: single | double | char | string

ScoreTransform — Score transformation function

Read-only: character vector | string scalar | function handle

This property is read-only.

Score transformation function describing how incremental learning functions transform raw response values, specified as a character vector, string scalar, or function handle. incrementalClassificationLinear stores the ScoreTransform value as a character vector or function handle.

This table describes the available built-in functions for score transformation.

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; for example, 'ScoreTransform',@function, where:

• function accepts an n-by-2 matrix (the original scores) and returns a matrix of the same size (the transformed scores). The column order corresponds to the class order in the ClassNames property.

• n is the number of observations, and row j of the matrix contains the class scores of observation j.

The default ScoreTransform value depends on how you create the model:

• If you convert a traditionally trained model to create Mdl, ScoreTransform is specified by the corresponding property of the traditionally trained model. For example, if the ScoreTransform property of the traditionally trained model is a score-to-posterior-probability transformation function, as computed by fitPosterior or fitSVMPosterior, Mdl.ScoreTransform contains an anonymous function.

• Otherwise, the default value is 'none' (when Learner is 'svm') or 'logit' (when Learner is 'logistic').

Data Types: char | string | function_handle

Training Parameters

EstimationPeriod — Number of observations processed to estimate hyperparameters

Read-only: nonnegative integer

This property is read-only.

Number of observations processed by the incremental model to estimate hyperparameters before training or tracking performance metrics, specified as a nonnegative integer.

Note

• If Mdl is prepared for incremental learning (all hyperparameters required for training are specified), incrementalClassificationLinear forces EstimationPeriod to 0.

• If Mdl is not prepared for incremental learning, incrementalClassificationLinear sets EstimationPeriod to 1000.

For more details, see Estimation Period.

Data Types: single | double

FitBias — Linear model intercept inclusion flag

Read-only: true | false

This property is read-only.

Linear model intercept inclusion flag, specified as true or false.

Value	Description
true	incrementalClassificationLinear includes the bias term β0 in the linear model, which incremental fitting functions fit to data.
false	incrementalClassificationLinear sets β0 = 0.

If Bias ≠ 0, FitBias must be true. In other words, incrementalClassificationLinear does not support an equality constraint on β₀.

The default FitBias value depends on how you create the model:

• If you convert a traditionally trained linear classification model object (ClassificationLinear) to create Mdl, FitBias is specified by the FitBias value of the ModelParameters property of the traditionally trained model.

• If you convert a linear model template object (returned by templateLinear) to create Mdl, FitBias is specified by the corresponding property of the object.

• Otherwise, the default value is true.

Data Types: logical

Mu — Predictor means

Read-only: vector of numeric values | []

This property is read-only.

Predictor means, specified as a numeric vector.

If Mu is an empty array [] and you specify 'Standardize',true, incremental fitting functions set Mu to the predictor variable means estimated during the estimation period specified by EstimationPeriod.

You cannot specify Mu directly.

Data Types: single | double

Sigma — Predictor standard deviations

Read-only: vector of numeric values | []

This property is read-only.

Predictor standard deviations, specified as a numeric vector.

If Sigma is an empty array [] and you specify 'Standardize',true, incremental fitting functions set Sigma to the predictor variable standard deviations estimated during the estimation period specified by EstimationPeriod.

You cannot specify Sigma directly.

Data Types: single | double

Solver — Objective function minimization technique

Read-only: 'scale-invariant' | 'sgd' | 'asgd'

This property is read-only.

Objective function minimization technique, specified as 'scale-invariant', 'sgd', or 'asgd'. incrementalClassificationLinear stores the Solver value as a character vector.

Value	Description	Notes
'scale-invariant'	Adaptive scale-invariant solver for incremental learning [1]	This algorithm is parameter free and can adapt to differences in predictor scales. Try this algorithm before using SGD or ASGD. To shuffle an incoming chunk of data before the fit function fits the model, set Shuffle to true.
'sgd'	Stochastic gradient descent (SGD) [3][2]	To train effectively with SGD, standardize the data and specify adequate values for hyperparameters using options listed in SGD and ASGD Solver Parameters. The fit function always shuffles an incoming chunk of data before fitting the model.
'asgd'	Average stochastic gradient descent (ASGD) [4]	To train effectively with ASGD, standardize the data and specify adequate values for hyperparameters using options listed in SGD and ASGD Solver Parameters. The fit function always shuffles an incoming chunk of data before fitting the model.

The default Solver value depends on how you create the model:

• If you create Mdl by calling incrementalClassificationLinear directly, the default value is 'scale-invariant'.

• If you convert a traditionally trained linear classification model object (ClassificationLinear) or a linear model template object (returned by templateLinear) to create Mdl, and the object uses ridge regularization and the SGD or ASGD solver, Mdl uses the same solver.

  (You can view the Solver value of a traditionally trained model (for example, TTMdl) in TTMdl.ModelParameters.Solver. For a model template object, you can view the Solver value by displaying the object in the Command Window or the Variables editor.)

• Otherwise, the Solver name-value argument of the incrementalLearner function sets this property. The default value of the argument is 'scale-invariant'.

Data Types: char | string

SolverOptions — Objective solver configurations

Read-only: structure array

This property is read-only.

Objective solver configurations, specified as a structure array. The fields of SolverOptions are properties specific to the specified solver Solver.

Data Types: struct

SGD and ASGD Solver Parameters

BatchSize — Mini-batch size

Read-only: positive integer

This property is read-only.

Mini-batch size, specified as a positive integer. At each learning cycle during training, incrementalClassificationLinear uses BatchSize observations to compute the subgradient.

The number of observations for the last mini-batch (last learning cycle in each function call of fit or updateMetricsAndFit) can be smaller than BatchSize. For example, if you supply 25 observations to fit or updateMetricsAndFit, the function uses 10 observations for the first two learning cycles and 5 observations for the last learning cycle.

The default BatchSize value depends on how you create the model:

• If you create Mdl by calling incrementalClassificationLinear directly, the default value is 10.

• If you convert a traditionally trained linear classification model object (ClassificationLinear) to create Mdl, and the object uses ridge regularization and the SGD or ASGD solver, BatchSize is specified by the BatchSize value of the ModelParameters property of the traditionally trained model.

• If you convert a linear model template object (returned by templateLinear) to create Mdl, and the object uses ridge regularization and the SGD or ASGD solver, BatchSize is specified by the corresponding property of the object.

• Otherwise, the BatchSize name-value argument of the incrementalLearner function sets this property. The default value of the argument is 10.

Data Types: single | double

Lambda — Ridge (L2) regularization term strength

Read-only: nonnegative scalar

This property is read-only.

Ridge (L2) regularization term strength, specified as a nonnegative scalar.

The default Lambda value depends on how you create the model:

• If you create Mdl by calling incrementalClassificationLinear directly, the default value is 1e-5.

• If you convert a traditionally trained linear classification model object (ClassificationLinear) or a linear model template object (returned by templateLinear) to create Mdl, and the object uses ridge regularization and the SGD or ASGD solver, Lambda is specified by the corresponding property of the object.

• Otherwise, the Lambda name-value argument of the incrementalLearner function sets this property. The default value of the argument is 1e-5.

Data Types: double | single

LearnRate — Initial learning rate

Read-only: 'auto' | positive scalar

This property is read-only.

Initial learning rate, specified as 'auto' or a positive scalar. incrementalClassificationLinear stores the LearnRate value as a numeric scalar.

The learning rate controls the optimization step size by scaling the objective subgradient. LearnRate specifies an initial value for the learning rate, and LearnRateSchedule determines the learning rate for subsequent learning cycles.

When you specify 'auto':

• The initial learning rate is 0.7.

• If EstimationPeriod > 0, fit and updateMetricsAndFit change the rate to 1/sqrt(1+max(sum(X.^2,obsDim))) at the end of EstimationPeriod. When the observations are the columns of the predictor data X collected during the estimation period, the obsDim value is 1; otherwise, the value is 2.

The default LearnRate value depends on how you create the model:

• If you create Mdl by calling incrementalClassificationLinear directly, the default value is 'auto'.

• If you convert a traditionally trained linear classification model object (ClassificationLinear) to create Mdl, and the object uses ridge regularization and the SGD or ASGD solver, LearnRate is specified by the LearnRate value of the ModelParameters property of the traditionally trained model.

• If you convert a linear model template object (returned by templateLinear) to create Mdl, and the object uses ridge regularization and the SGD or ASGD solver, LearnRate is specified by the corresponding property of the object.

• Otherwise, the LearnRate name-value argument of the incrementalLearner function sets this property. The default value of the argument is 'auto'.

Data Types: single | double | char | string

LearnRateSchedule — Learning rate schedule

Read-only: 'decaying' | 'constant'

This property is read-only.

Learning rate schedule, specified as 'decaying' or 'constant', where LearnRate specifies the initial learning rate ɣ₀. incrementalClassificationLinear stores the LearnRateSchedule value as a character vector.

Value	Description
'constant'	The learning rate is ɣ0 for all learning cycles.
'decaying'	The learning rate at learning cycle t is $${\gamma }_t=\frac{{\gamma }_0}{{\left(1+\lambda {\gamma }_{0}t\right)}^c}.$$ λ is the value of Lambda. If Solver is 'sgd', then c = 1. If Solver is 'asgd', then c is 0.75 [4].

The default LearnRateSchedule value depends on how you create the model:

• If you convert a traditionally trained model object or template model object to create Mdl, the LearnRateSchedule name-value argument of the incrementalLearner function sets this property. The default value of the argument is 'decaying'.

• Otherwise, the default value is 'decaying'.

Data Types: char | string

Performance Metrics Parameters

IsWarm — Flag indicating whether model tracks performance metrics

Read-only: false or 0 | true or 1

This property is read-only.

Flag indicating whether the incremental model tracks performance metrics, specified as logical 0 (false) or 1 (true).

The incremental model Mdl is warm (IsWarm becomes true) after incremental fitting functions fit (EstimationPeriod + MetricsWarmupPeriod) observations to the incremental model.

Value	Description
true or 1	The incremental model Mdl is warm. Consequently, updateMetrics and updateMetricsAndFit track performance metrics in the Metrics property of Mdl.
false or 0	updateMetrics and updateMetricsAndFit do not track performance metrics.

Data Types: logical

Metrics — Model performance metrics

Read-only: table

This property is read-only.

Model performance metrics updated during incremental learning by updateMetrics and updateMetricsAndFit, specified as a table with two columns and m rows, where m is the number of metrics specified by the Metrics name-value argument.

The columns of Metrics are labeled Cumulative and Window.

• Cumulative: Element j is the model performance, as measured by metric j, from the time the model became warm (IsWarm is 1).

• Window: Element j is the model performance, as measured by metric j, evaluated over all observations within the window specified by the MetricsWindowSize property. The software updates Window after it processes MetricsWindowSize observations.

Rows are labeled by the specified metrics. For details, see the Metrics name-value argument of incrementalLearner or incrementalClassificationLinear.

Data Types: table

MetricsWarmupPeriod — Number of observations fit before tracking performance metrics

Read-only: nonnegative integer

This property is read-only.

Number of observations the incremental model must be fit to before it tracks performance metrics in its Metrics property, specified as a nonnegative integer.

The default MetricsWarmupPeriod value depends on how you create the model:

• If you convert a traditionally trained model to create Mdl, the MetricsWarmupPeriod name-value argument of the incrementalLearner function sets this property. The default value of the argument is 0.

• Otherwise, the default value is 1000.

For more details, see Performance Metrics.

Data Types: single | double

MetricsWindowSize — Number of observations to use to compute window performance metrics

Read-only: positive integer

This property is read-only.

Number of observations to use to compute window performance metrics, specified as a positive integer.

The default MetricsWindowSize value depends on how you create the model:

• If you convert a traditionally trained model to create Mdl, the MetricsWindowSize name-value argument of the incrementalLearner function sets this property. The default value of the argument is 200.

• Otherwise, the default value is 200.

For more details on performance metrics options, see Performance Metrics.

Data Types: single | double

Object Functions

fit	Train linear model for incremental learning
updateMetricsAndFit	Update performance metrics in linear incremental learning model given new data and train model
updateMetrics	Update performance metrics in linear incremental learning model given new data
loss	Loss of linear incremental learning model on batch of data
predict	Predict responses for new observations from linear incremental learning model
perObservationLoss	Per observation classification error of model for incremental learning
reset	Reset incremental classification model

Examples

Create Incremental Learner Without Any Prior Information

Create a default incremental linear SVM model for binary classification.

Mdl = incrementalClassificationLinear()

Mdl = 
  incrementalClassificationLinear

            IsWarm: 0
           Metrics: [1×2 table]
        ClassNames: [1×0 double]
    ScoreTransform: 'none'
              Beta: [0×1 double]
              Bias: 0
           Learner: 'svm'


  Properties, Methods

Mdl is an incrementalClassificationLinear model object. All its properties are read-only.

Mdl must be fit to data before you can use it to perform any other operations.

Load the human activity data set. Randomly shuffle the data.

load humanactivity
n = numel(actid);
rng(1) % For reproducibility
idx = randsample(n,n);
X = feat(idx,:);
Y = actid(idx);

For details on the data set, enter Description at the command line.

Responses can be one of five classes: Sitting, Standing, Walking, Running, or Dancing. Dichotomize the response by identifying whether the subject is moving (actid > 2).

Y = Y > 2;

Fit the incremental model to the training data by using the updateMetricsAndFit function. Simulate a data stream by processing chunks of 50 observations at a time. At each iteration:

• Process 50 observations.

• Overwrite the previous incremental model with a new one fitted to the incoming observations.

• Store ${\beta }_1$, the cumulative metrics, and the window metrics to see how they evolve during incremental learning.

% Preallocation
numObsPerChunk = 50;
nchunk = floor(n/numObsPerChunk);
ce = array2table(zeros(nchunk,2),'VariableNames',["Cumulative" "Window"]);
beta1 = zeros(nchunk+1,1);    

% Incremental learning
for j = 1:nchunk
    ibegin = min(n,numObsPerChunk*(j-1) + 1);
    iend   = min(n,numObsPerChunk*j);
    idx = ibegin:iend;    
    Mdl = updateMetricsAndFit(Mdl,X(idx,:),Y(idx));
    ce{j,:} = Mdl.Metrics{"ClassificationError",:};
    beta1(j + 1) = Mdl.Beta(1);
end

IncrementalMdl is an incrementalClassificationLinear model object trained on all the data in the stream. During incremental learning and after the model is warmed up, updateMetricsAndFit checks the performance of the model on the incoming observations, and then fits the model to those observations.

To see how the performance metrics and ${\beta }_1$ evolve during training, plot them on separate tiles.

t = tiledlayout(2,1);
nexttile
plot(beta1)
ylabel('\beta_1')
xlim([0 nchunk])
nexttile
h = plot(ce.Variables);
xlim([0 nchunk])
ylabel('Classification Error')
xline(Mdl.MetricsWarmupPeriod/numObsPerChunk,'g-.')
legend(h,ce.Properties.VariableNames)
xlabel(t,'Iteration')

The plot suggests that updateMetricsAndFit does the following:

• Fit ${\beta }_1$ during all incremental learning iterations.

• Compute the performance metrics after the metrics warm-up period only.

• Compute the cumulative metrics during each iteration.

• Compute the window metrics after processing 200 observations (4 iterations).

Configure Incremental Learning Options

Prepare an incremental binary SVM learner by specifying a metrics warm-up period, during which the updateMetricsAndFit function only fits the model. Specify a metrics window size of 500 observations. Train the model by using SGD, and adjust the SGD batch size, learning rate, and regularization parameter.

Load the human activity data set. Randomly shuffle the data.

load humanactivity
n = numel(actid);
rng("default") % For reproducibility
idx = randsample(n,n);
X = feat(idx,:);
Y = actid(idx);

For details on the data set, enter Description at the command line.

Responses can be one of five classes: Sitting, Standing, Walking, Running, or Dancing. Dichotomize the response by identifying whether the subject is moving (actid > 2).

Y = Y > 2;

Create an incremental linear model for binary classification. Configure the model as follows:

• Specify that the incremental fitting functions process the raw (unstandardized) predictor data.

• Specify the SGD solver.

• Assume that a ridge regularization parameter value of 0.001, SGD batch size of 20, and learning rate of 0.002 work well for the problem.

• Specify a metrics warm-up period of 5000 observations.

• Specify a metrics window size of 500 observations.

• Track the classification and hinge error metrics to measure the performance of the model.

Mdl = incrementalClassificationLinear('Standardize',false, ...
    'Solver','sgd','Lambda',0.001,'BatchSize',20,'LearnRate',0.002, ...
    'MetricsWarmupPeriod',5000,'MetricsWindowSize',500, ...
    'Metrics',{'classiferror' 'hinge'})

Mdl = 
  incrementalClassificationLinear

            IsWarm: 0
           Metrics: [2×2 table]
        ClassNames: [1×0 double]
    ScoreTransform: 'none'
              Beta: [0×1 double]
              Bias: 0
           Learner: 'svm'


  Properties, Methods

Mdl is an incrementalClassificationLinear model object configured for incremental learning.

Fit the incremental model to the rest of the data by using the updateMetricsAndFit function. At each iteration:

• Simulate a data stream by processing a chunk of 50 observations. Note that the chunk size is different from SGD batch size.

• Overwrite the previous incremental model with a new one fitted to the incoming observations.

• Store the estimated coefficient ${\beta }_{10}$, the cumulative metrics, and the window metrics to see how they evolve during incremental learning.

% Preallocation
numObsPerChunk = 50;
nchunk = floor(n/numObsPerChunk);
ce = array2table(zeros(nchunk,2),'VariableNames',["Cumulative" "Window"]);
hinge = array2table(zeros(nchunk,2),'VariableNames',["Cumulative" "Window"]);
beta10 = zeros(nchunk+1,1);    

% Incremental fitting
for j = 1:nchunk
    ibegin = min(n,numObsPerChunk*(j-1) + 1);
    iend   = min(n,numObsPerChunk*j);
    idx = ibegin:iend;    
    Mdl = updateMetricsAndFit(Mdl,X(idx,:),Y(idx));
    ce{j,:} = Mdl.Metrics{"ClassificationError",:};
    hinge{j,:} = Mdl.Metrics{"HingeLoss",:};
    beta10(j + 1) = Mdl.Beta(10);
end

Mdl is an incrementalClassificationLinear model object trained on all the data in the stream. During incremental learning and after the model is warmed up, updateMetricsAndFit checks the performance of the model on the incoming observations, and then fits the model to those observations.

To see how the performance metrics and ${\beta }_{10}$ evolve during training, plot them on separate tiles.

tiledlayout(2,2)
nexttile
plot(beta10)
ylabel('\beta_{10}')
xlim([0 nchunk]);
xline(Mdl.MetricsWarmupPeriod/numObsPerChunk,'g-.')
xlabel('Iteration')
nexttile
h = plot(ce.Variables);
xlim([0 nchunk]);
ylabel('Classification Error')
xline(Mdl.MetricsWarmupPeriod/numObsPerChunk,'g-.')
legend(h,ce.Properties.VariableNames)
xlabel('Iteration')
nexttile
h = plot(hinge.Variables);
xlim([0 nchunk]);
ylabel('Hinge Loss')
xline(Mdl.MetricsWarmupPeriod/numObsPerChunk,'g-.')
legend(h,hinge.Properties.VariableNames)
xlabel('Iteration')

The plot suggests that updateMetricsAndFit does the following:

• Fit ${\beta }_{10}$ during all incremental learning iterations.

• Compute the performance metrics after the metrics warm-up period only.

• Compute the cumulative metrics during each iteration.

• Compute the window metrics after processing 500 observations (10 iterations).

Convert Traditionally Trained Model to Incremental Learner

Train a linear model for binary classification by using fitclinear, convert it to an incremental learner, track its performance, and fit it to streaming data. Carry over training options from traditional to incremental learning.

Load and Preprocess Data

Load the human activity data set. Randomly shuffle the data. Orient the observations of the predictor data in columns.

load humanactivity
rng(1); % For reproducibility
n = numel(actid);
idx = randsample(n,n);
X = feat(idx,:)';
Y = actid(idx);

For details on the data set, enter Description at the command line.

Responses can be one of five classes: Sitting, Standing, Walking, Running, or Dancing. Dichotomize the response by identifying whether the subject is moving (actid > 2).

Y = Y > 2;

Suppose that the data collected when the subject was idle (Y = false) has double the quality than when the subject was moving. Create a weight variable that attributes 2 to observations collected from an idle subject, and 1 to a moving subject.

W = ones(n,1) + ~Y;

Train Linear Model for Binary Classification

Fit a linear model for binary classification to a random sample of half the data.

idxtt = randsample([true false],n,true);
TTMdl = fitclinear(X(:,idxtt),Y(idxtt),'ObservationsIn','columns', ...
    'Weights',W(idxtt))

TTMdl = 
  ClassificationLinear
      ResponseName: 'Y'
        ClassNames: [0 1]
    ScoreTransform: 'none'
              Beta: [60×1 double]
              Bias: -0.1107
            Lambda: 8.2967e-05
           Learner: 'svm'


  Properties, Methods

TTMdl is a ClassificationLinear model object representing a traditionally trained linear model for binary classification.

Convert Trained Model

Convert the traditionally trained classification model to a binary classification linear model for incremental learning.

IncrementalMdl = incrementalLearner(TTMdl)

IncrementalMdl = 
  incrementalClassificationLinear

            IsWarm: 1
           Metrics: [1×2 table]
        ClassNames: [0 1]
    ScoreTransform: 'none'
              Beta: [60×1 double]
              Bias: -0.1107
           Learner: 'svm'


  Properties, Methods

Separately Track Performance Metrics and Fit Model

Perform incremental learning on the rest of the data by using the updateMetrics and fit functions. Simulate a data stream by processing 50 observations at a time. At each iteration:

1. Call updateMetrics to update the cumulative and window classification error of the model given the incoming chunk of observations. Overwrite the previous incremental model to update the losses in the Metrics property. Note that the function does not fit the model to the chunk of data—the chunk is "new" data for the model. Specify that the observations are oriented in columns, and specify the observation weights.

2. Call fit to fit the model to the incoming chunk of observations. Overwrite the previous incremental model to update the model parameters. Specify that the observations are oriented in columns, and specify the observation weights.

3. Store the classification error and first estimated coefficient ${\beta }_1$.

% Preallocation
idxil = ~idxtt;
nil = sum(idxil);
numObsPerChunk = 50;
nchunk = floor(nil/numObsPerChunk);
ce = array2table(zeros(nchunk,2),'VariableNames',["Cumulative" "Window"]);
beta1 = [IncrementalMdl.Beta(1); zeros(nchunk,1)];
Xil = X(:,idxil);
Yil = Y(idxil);
Wil = W(idxil);

% Incremental fitting
for j = 1:nchunk
    ibegin = min(nil,numObsPerChunk*(j-1) + 1);
    iend   = min(nil,numObsPerChunk*j);
    idx = ibegin:iend;
    IncrementalMdl = updateMetrics(IncrementalMdl,Xil(:,idx),Yil(idx), ...
        'ObservationsIn','columns','Weights',Wil(idx));
    ce{j,:} = IncrementalMdl.Metrics{"ClassificationError",:};
    IncrementalMdl = fit(IncrementalMdl,Xil(:,idx),Yil(idx), ...
        'ObservationsIn','columns','Weights',Wil(idx));
    beta1(j + 1) = IncrementalMdl.Beta(end);
end

IncrementalMdl is an incrementalClassificationLinear model object trained on all the data in the stream.

Alternatively, you can use updateMetricsAndFit to update performance metrics of the model given a new chunk of data, and then fit the model to the data.

Plot a trace plot of the performance metrics and estimated coefficient ${\beta }_1$.

t = tiledlayout(2,1);
nexttile
h = plot(ce.Variables);
xlim([0 nchunk])
ylabel('Classification Error')
legend(h,ce.Properties.VariableNames)
nexttile
plot(beta1)
ylabel('\beta_1')
xlim([0 nchunk])
xlabel(t,'Iteration')

The cumulative loss is stable and decreases gradually, whereas the window loss jumps.

${\beta }_1$ changes abruptly at first, then gradually levels off as fit processes more chunks.

More About

Incremental Learning

Incremental learning, or online learning, is a branch of machine learning concerned with processing incoming data from a data stream, possibly given little to no knowledge of the distribution of the predictor variables, aspects of the prediction or objective function (including tuning parameter values), or whether the observations are labeled. Incremental learning differs from traditional machine learning, where enough labeled data is available to fit to a model, perform cross-validation to tune hyperparameters, and infer the predictor distribution.

Given incoming observations, an incremental learning model processes data in any of the following ways, but usually in this order:

• Predict labels.

• Measure the predictive performance.

• Check for structural breaks or drift in the model.

• Fit the model to the incoming observations.

For more details, see Incremental Learning Overview.

Adaptive Scale-Invariant Solver for Incremental Learning

The adaptive scale-invariant solver for incremental learning, introduced in [1], is a gradient-descent-based objective solver for training linear predictive models. The solver is hyperparameter free, insensitive to differences in predictor variable scales, and does not require prior knowledge of the distribution of the predictor variables. These characteristics make it well suited to incremental learning.

The standard SGD and ASGD solvers are sensitive to differing scales among the predictor variables, resulting in models that can perform poorly. To achieve better accuracy using SGD and ASGD, you can standardize the predictor data, and tune the regularization and learning rate parameters. For traditional machine learning, enough data is available to enable hyperparameter tuning by cross-validation and predictor standardization. However, for incremental learning, enough data might not be available (for example, observations might be available only one at a time) and the distribution of the predictors might be unknown. These characteristics make parameter tuning and predictor standardization difficult or impossible to do during incremental learning.

The incremental fitting functions for classification fit and updateMetricsAndFit use the more aggressive ScInOL2 version of the algorithm.

Tips

• After creating a model, you can generate C/C++ code that performs incremental learning on a data stream. Generating C/C++ code requires MATLAB Coder™. For details, see Introduction to Code Generation.

Algorithms

Estimation Period

During the estimation period, the incremental fitting functions fit and updateMetricsAndFit use the first incoming EstimationPeriod observations to estimate (tune) hyperparameters required for incremental training. Estimation occurs only when EstimationPeriod is positive. This table describes the hyperparameters and when they are estimated, or tuned.

Hyperparameter	Model Property	Usage	Conditions
Predictor means and standard deviations	Mu and Sigma	Standardize predictor data	The hyperparameters are estimated when both of these conditions apply: Incremental fitting functions are configured to standardize predictor data (see Standardize Data). Mdl.Mu and Mdl.Sigma are empty arrays [].
Learning rate	LearnRate	Adjust the solver step size	The hyperparameter is estimated when both of these conditions apply: The solver is SGD or ASGD (see Solver). You do not specify the 'LearnRate' name-value argument as a positive scalar.

During the estimation period, fit does not fit the model, and updateMetricsAndFit does not fit the model or update the performance metrics. At the end of the estimation period, the functions update the properties that store the hyperparameters.

Standardize Data

If incremental learning functions are configured to standardize predictor variables, they do so using the means and standard deviations stored in the Mu and Sigma properties of the incremental learning model Mdl.

• When you set 'Standardize',true and a positive estimation period (see EstimationPeriod), and Mdl.Mu and Mdl.Sigma are empty, incremental fitting functions estimate means and standard deviations using the estimation period observations.

• When you set 'Standardize','auto' (the default), the following conditions apply:

  • If you create incrementalClassificationLinear by converting a traditionally trained binary linear SVM model (ClassificationSVM or CompactClassificationSVM), and the Mu and Sigma properties of the traditionally trained model are empty arrays [], incremental learning functions do not standardize predictor variables. If the Mu and Sigma properties of the traditionally trained model are nonempty, incremental learning functions standardize the predictor variables using the specified means and standard deviations. Incremental fitting functions do not estimate new means and standard deviations, regardless of the length of the estimation period.

  • If you create incrementalClassificationLinear by converting a linear classification model (ClassificationLinear), incremental learning functions do not standardize the data, regardless of the length of the estimation period.

  • If you do not convert a traditionally trained model, incremental learning functions standardize the predictor data only when you specify an SGD solver (see Solver) and a positive estimation period (see EstimationPeriod).

• When incremental fitting functions estimate predictor means and standard deviations, the functions compute weighted means and weighted standard deviations using the estimation period observations. Specifically, the functions standardize predictor j (x_j) using

  $$x_j^{\ast }=\frac{x_j-{\mu }_j^{\ast }}{{\sigma }_j^{\ast }}.$$

  • x_j is predictor j, and x_jk is observation k of predictor j in the estimation period.

  • $${\mu }_j^{\ast }=\frac{1}{{\displaystyle \sum _k{w}_k^{\ast }}}{\displaystyle \sum _k{w}_k^{\ast }{x}_{jk}}.$$

  • $${\left({\sigma }_j^{\ast }\right)}^2=\frac{1}{{\displaystyle \sum _k{w}_k^{\ast }}}{\displaystyle \sum _k{w}_k^{\ast }{\left(x_{jk}-{\mu }_j^{\ast }\right)}^2}.$$

  • $$w_j^{\ast }=\frac{w_j}{{\displaystyle \sum _{\forall j\in \text{Class }k}{w}_j}}{p}_k,$$

    • p_k is the prior probability of class k (Prior property of the incremental model).

    • w_j is observation weight j.

Performance Metrics

• The updateMetrics and updateMetricsAndFit functions track model performance metrics ('Metrics') from new data when the incremental model is warm (IsWarm property). An incremental model becomes warm after fit or updateMetricsAndFit fit the incremental model to MetricsWarmupPeriod observations, which is the metrics warm-up period.

  If EstimationPeriod > 0, the functions estimate hyperparameters before fitting the model to data. Therefore, the functions must process an additional EstimationPeriod observations before the model starts the metrics warm-up period.

• The Metrics property of the incremental model stores two forms of each performance metric as variables (columns) of a table, Cumulative and Window, with individual metrics in rows. When the incremental model is warm, updateMetrics and updateMetricsAndFit update the metrics at the following frequencies:

  • Cumulative — The functions compute cumulative metrics since the start of model performance tracking. The functions update metrics every time you call the functions and base the calculation on the entire supplied data set.

  • Window — The functions compute metrics based on all observations within a window determined by the MetricsWindowSize name-value pair argument. MetricsWindowSize also determines the frequency at which the software updates Window metrics. For example, if MetricsWindowSize is 20, the functions compute metrics based on the last 20 observations in the supplied data (X((end – 20 + 1):end,:) and Y((end – 20 + 1):end)).

    Incremental functions that track performance metrics within a window use the following process:

    1. Store a buffer of length MetricsWindowSize for each specified metric, and store a buffer of observation weights.

    2. Populate elements of the metrics buffer with the model performance based on batches of incoming observations, and store corresponding observation weights in the weights buffer.

    3. When the buffer is filled, overwrite Mdl.Metrics.Window with the weighted average performance in the metrics window. If the buffer is overfilled when the function processes a batch of observations, the latest incoming MetricsWindowSize observations enter the buffer, and the earliest observations are removed from the buffer. For example, suppose MetricsWindowSize is 20, the metrics buffer has 10 values from a previously processed batch, and 15 values are incoming. To compose the length 20 window, the functions use the measurements from the 15 incoming observations and the latest 5 measurements from the previous batch.

• The software omits an observation with a NaN score when computing the Cumulative and Window performance metric values.

References

[1] Kempka, Michał, Wojciech Kotłowski, and Manfred K. Warmuth. "Adaptive Scale-Invariant Online Algorithms for Learning Linear Models." Preprint, submitted February 10, 2019. https://arxiv.org/abs/1902.07528.

[2] Langford, J., L. Li, and T. Zhang. “Sparse Online Learning Via Truncated Gradient.” J. Mach. Learn. Res., Vol. 10, 2009, pp. 777–801.

[3] Shalev-Shwartz, S., Y. Singer, and N. Srebro. “Pegasos: Primal Estimated Sub-Gradient Solver for SVM.” Proceedings of the 24th International Conference on Machine Learning, ICML ’07, 2007, pp. 807–814.

[4] Xu, Wei. “Towards Optimal One Pass Large Scale Learning with Averaged Stochastic Gradient Descent.” CoRR, abs/1107.2490, 2011.

Extended Capabilities

C/C++ Code Generation

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

Usage notes and limitations:

• All object functions of an incrementalClassificationLinear model object, except for perObservationLoss and reset, support code generation.

• If you configure Mdl to shuffle data (see Solver and Shuffle), the fit function randomly shuffles each incoming batch of observations before it fits the model to the batch. The order of the shuffled observations might not match the order generated by MATLAB.

• When you generate code that loads or creates an incrementalClassificationLinear model object, the following restrictions apply.

  • Mdl cannot represent a converted SVM model configured to return posterior probabilities as scores.

  • The ClassNames property must contain all expected class names.

  • The NumPredictors property must reflect the number of predictor variables.

For more information, see Introduction to Code Generation.

Version History

Introduced in R2020b