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.
incrementalClassificationLinear | Binary classification linear model for incremental learning |
incrementalClassificationNaiveBayes | Naive Bayes classification model for incremental learning |
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 Classification Using Succinct Workflow
Use the succinct workflow to implement incremental learning for binary classification with prequential evaluation.
Implement Incremental Learning for Classification Using Flexible Workflow
Use the flexible workflow to implement incremental learning for binary classification with prequential evaluation.
Train a logistic regression model using the Classification Learner app, and then initialize an incremental model for binary classification using the estimated coefficients.
Perform Conditional Training during Incremental Learning
Use the flexible workflow to implement conditional training during incremental learning with a naive Bayes multiclass classification model.