Incremental Learning

Fit linear model for regression to streaming data and track its performance

Incremental learning, or online learning, involves processing incoming data from a data stream, possibly given little to no knowledge of the distribution of the predictor variables, aspects of the objective function, and whether the observations are labeled. Incremental learning problems contrast with traditional machine learning methods, in which enough labeled data is available to fit to a model, perform cross-validation to tune hyperparameters, and infer the predictor distribution characteristics.

Incremental learning requires a configured incremental model. You can create and configure an incremental model directly by calling an object, for example incrementalRegressionLinear, or you can convert a supported traditionally trained model to an incremental learner by using incrementalLearner. After configuring a model and setting up a data stream, you can fit the incremental model to the incoming chunks of data, track the predictive performance of the model, or perform both actions simultaneously.

For more details, see Incremental Learning Overview.

You can also incrementally monitor for drift in concept data, such as regression loss. First you need to configure the drift detector using incrementalConceptDriftDetector. After setting up a data stream, you can update the drift detector and check for any drift using detectdrift. For more information, see the reference pages.

Functions

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Drift-Aware Learning

Create Incremental Drift-Aware Model

incrementalDriftAwareLearner Construct drift-aware model for incremental learning

Incrementally Fit and Track Performance

`fit`	Train drift-aware learner for incremental learning with new data
`updateMetrics`	Update performance metrics in incremental drift-aware learning model given new data
`updateMetricsAndFit`	Update performance metrics in incremental drift-aware learning model given new data and train model

Other Model Operations

`loss`	Regression or classification error of incremental drift-aware learner
`perObservationLoss`	Per observation regression or classification error of incremental drift-aware learner
`predict`	Predict responses for new observations from incremental drift-aware learning model
`reset`	Reset incremental drift-aware learner

Kernel Regression Model

Create Incremental Model

`incrementalRegressionKernel`	Kernel regression model for incremental learning
`incrementalLearner`	Convert kernel regression model to incremental learner

Incrementally Fit and Track Performance

`fit`	Train kernel model for incremental learning
`updateMetrics`	Update performance metrics in kernel incremental learning model given new data
`updateMetricsAndFit`	Update performance metrics in kernel incremental learning model given new data and train model

Other Model Operations

`predict`	Predict responses for new observations from kernel incremental learning model
`loss`	Loss of kernel incremental learning model on batch of data
`perObservationLoss`	Per observation regression error of model for incremental learning
`reset`	Reset incremental regression model

Linear Regression Model

Create Incremental Model

`incrementalRegressionLinear`	Linear regression model for incremental learning
`incrementalLearner`	Convert support vector machine (SVM) regression model to incremental learner
`incrementalLearner`	Convert linear regression model to incremental learner

Incrementally Fit and Track Performance

`fit`	Train linear model for incremental learning
`updateMetrics`	Update performance metrics in linear incremental learning model given new data
`updateMetricsAndFit`	Update performance metrics in linear incremental learning model given new data and train model

Other Model Operations

`predict`	Predict responses for new observations from linear incremental learning model
`loss`	Loss of linear incremental learning model on batch of data
`perObservationLoss`	Per observation regression error of model for incremental learning
`reset`	Reset incremental regression model

Concept Drift Detection

Create Concept Drift Detector

incrementalConceptDriftDetector Instantiate incremental concept drift detector

Detect Drift and Reset Model

`detectdrift`	Update drift detector states and drift status with new data
`reset`	Reset incremental concept drift detector

Objects

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Incremental Concept Drift Detectors

`DriftDetectionMethod`	Incremental drift detector that utilizes Drift Detection Method (DDM)
`HoeffdingDriftDetectionMethod`	Incremental concept drift detector that utilizes Hoeffding's Bounds Drift Detection Method (HDDM)

Topics

Incremental Learning Overview
Discover fundamental concepts about incremental learning, including incremental learning objects, functions, and workflows.
Configure Incremental Learning Model
Prepare an incremental learning model for incremental performance evaluation and training on a data stream.
Implement Incremental Learning for Regression Using Succinct Workflow
Use the succinct workflow to implement incremental learning for linear regression with prequential evaluation.
Implement Incremental Learning for Regression Using Flexible Workflow
Use a flexible workflow to implement incremental learning for linear regression with prequential evaluation.
Initialize Incremental Learning Model from SVM Regression Model Trained in Regression Learner
Train a linear SVM regression model using the Regression Learner app, and then initialize an incremental model for regression using the estimated coefficients.