Incremental Learning

Fit linear model for binary classification to streaming data, and track its performance

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

Functions

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Convert Traditionally Trained Model

`incrementalLearner`	Convert binary classification support vector machine (SVM) model to incremental learner
`incrementalLearner`	Convert linear model for binary classification to incremental learner

Incrementally Fit or Track Performance

`fit`	Train incremental learning model
`updateMetrics`	Update incremental learning model performance metrics on new data
`updateMetricsAndFit`	Update incremental learning model performance metrics on new data, then train model

Other Model Operations

`predict`	Predict responses for new observations from incremental learning model
`loss`	Loss of incremental learning model on batch of data

Objects

incrementalClassificationLinear Binary classification linear model for incremental learning

Topics

Incremental Learning Overview

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.

Configure Incremental Learning Model

Prepare incremental learning model for incremental performance evaluation and training on data stream.

Implement Incremental Learning Using Succinct Workflow

This example shows how to use the succinct workflow to implement incremental learning for binary classification with prequential evaluation.

Implement Incremental Learning Using Flexible Workflow

This example shows how to use the flexible workflow to implement incremental learning for binary classification with prequential evaluation.

Initialize Incremental Learning Model from Logistic Regression Model Trained in Classification Learner

Train a logistic regression model using Classification Learner, then initialize an incremental model for binary classification using the estimated coefficients.

Documentation