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.
|Convert binary classification support vector machine (SVM) model to incremental learner|
|Convert linear model for binary classification to incremental learner|
|Convert naive Bayes classification model to incremental learner|
|Train linear model for incremental learning|
|Update performance metrics in linear model for incremental learning given new data|
|Update performance metrics in linear model for incremental learning given new data and train model|
|Train naive Bayes classification model for incremental learning|
|Update performance metrics in naive Bayes classification model for incremental learning given new data|
|Update performance metrics in naive Bayes classification model for incremental learning given new data and train model|
|Predict responses for new observations from linear model for incremental learning|
|Loss of linear model for incremental learning on batch of data|
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 binary classification with prequential evaluation.
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.
Use the flexible workflow to implement conditional training during incremental learning with a naive Bayes multiclass classification model.
This example shows how to incrementally train a model to classify documents based on word frequencies in the documents; a bag-of-words model.
This example shows how to prepare heterogeneous predictor data, containing real-valued and categorical measurements, for incremental learning using a naive Bayes classifier.