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 using
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.
Convert Traditionally Trained Model
Incrementally Fit and Track Performance
|Linear regression model for incremental learning|
Discover fundamental concepts about incremental learning, including incremental learning objects, functions, and workflows.
Prepare an incremental learning model for incremental performance evaluation and training on a data stream.
Use the succinct workflow to implement incremental learning for linear regression with prequential evaluation.
Use the flexible workflow to implement incremental learning for linear regression with prequential evaluation.
Train a linear SVM regression model using the Regression Learner app, and then initialize an incremental model for regression using the estimated coefficients.