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Incremental Learning

Fit classification model 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 incrementalClassificationLinear, 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 classification error. 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 Binary Classification Model

incrementalLearnerConvert kernel model for binary classification to incremental learner

Linear Binary Classification Model

incrementalLearnerConvert binary classification support vector machine (SVM) model to incremental learner
incrementalLearnerConvert linear model for binary classification to incremental learner

Multiclass ECOC Classification Model

incrementalLearnerConvert multiclass error-correcting output codes (ECOC) model to incremental learner

Naive Bayes Model

incrementalLearnerConvert naive Bayes classification model to incremental learner

Kernel Binary Classification 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 Binary Classification 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

Multiclass ECOC Classification Model

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

Naive Bayes Model

fitTrain naive Bayes classification model for incremental learning
updateMetricsUpdate performance metrics in naive Bayes incremental learning classification model given new data
updateMetricsAndFitUpdate performance metrics in naive Bayes incremental learning classification model given new data and train model

Kernel Binary Classification Model

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

Linear Binary Classification Model

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

Multiclass ECOC Classification Model

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

Naive Bayes Model

predictPredict responses for new observations from naive Bayes incremental learning classification model
lossLoss of naive Bayes incremental learning classification model on batch of data
logpLog unconditional probability density of naive Bayes classification model for incremental learning
perObservationLossPer observation classification error of model for incremental learning
resetReset incremental classification 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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incrementalClassificationKernel Binary classification kernel model for incremental learning
incrementalClassificationLinearBinary classification linear model for incremental learning
incrementalClassificationECOC Multiclass classification model using binary learners for incremental learning
incrementalClassificationNaiveBayesNaive Bayes classification 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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