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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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Kernel Regression Model

incrementalLearnerConvert kernel regression model to incremental learner

Linear Regression Model

incrementalLearnerConvert support vector machine (SVM) regression model to incremental learner
incrementalLearnerConvert linear regression model to incremental learner

Kernel Regression Model

fitTrain kernel model for incremental learning
updateMetricsUpdate performance metrics in kernel incremental learning model given new data
updateMetricsAndFitUpdate performance metrics in kernel incremental learning model given new data and train model

Linear Regression Model

fitTrain linear model for incremental learning
updateMetricsUpdate performance metrics in linear incremental learning model given new data
updateMetricsAndFitUpdate performance metrics in linear incremental learning model given new data and train model

Kernel Regression Model

predictPredict responses for new observations from kernel incremental learning model
lossLoss of kernel incremental learning model on batch of data
perObservationLossPer observation regression error of model for incremental learning
resetReset incremental regression model

Linear Regression Model

predictPredict responses for new observations from linear incremental learning model
lossLoss of linear incremental learning model on batch of data
perObservationLossPer observation regression error of model for incremental learning
resetReset incremental regression model
incrementalConceptDriftDetectorInstantiate incremental concept drift detector
detectdriftUpdate drift detector states and drift status with new data
resetReset incremental concept drift detector

Objects

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incrementalRegressionKernelKernel regression model for incremental learning
incrementalRegressionLinearLinear regression model for incremental learning
DriftDetectionMethodIncremental drift detector that utilizes Drift Detection Method (DDM)
HoeffdingDriftDetectionMethodIncremental concept drift detector that utilizes Hoeffding's Bounds Drift Detection Method (HDDM)

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