Incremental learning, or online learning, is concerned with 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, or even whether the observations are labeled. The 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
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 its predictive performance, or perform both actions simultaneously.
For more details, see Incremental Learning Overview
|Binary classification linear model for incremental learning|
Incremental learning, or online learning, is a branch of machine learning concerned with processing incoming data from a data stream — continuously and in real time — possibly given little to no knowledge of the distribution of the predictor variables, the sample size, aspects of the prediction or objective function (including adequate tuning parameter values), or whether the observations have labeled.
Prepare incremental learning model for incremental performance evaluation and training on data stream.
This example shows how to use the succinct workflow to implement incremental learning for binary classification with prequential evaluation.
This example shows how to use the flexible workflow to implement incremental learning for binary classification with prequential evaluation.
Train a logistic regression model using Classification Learner, then initialize an incremental model for binary classification using the estimated coefficients.