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增量学习

将分类模型与流化数据进行拟合并跟踪其性能

增量学习(或在线学习)需要处理来自数据流的传入数据,且可能对预测变量的分布、目标函数的各个方面,以及观测值是否带标签等知识都知之甚少或完全不了解。增量学习问题与传统的机器学习方法形成对比,在传统的机器学习方法中,有足够的带标签数据可用于模型拟合、执行交叉验证以调整超参数并推断预测变量分布特性。

增量学习需要经过配置的增量模型。您可以通过调用一个对象(例如 incrementalClassificationLinear)直接创建和配置增量模型,也可以使用 incrementalLearner 将受支持的以传统方式训练的模型转换为增量学习器。在配置模型并设置数据流后,您可以将增量模型与传入的数据块进行拟合,跟踪该模型的预测性能,或同时执行这两项操作。

有关详细信息,请参阅Incremental Learning Overview

函数

全部展开

线性二类分类模型

incrementalLearnerConvert binary classification support vector machine (SVM) model to incremental learner
incrementalLearnerConvert linear model for binary classification to incremental learner

朴素贝叶斯模型

incrementalLearnerConvert naive Bayes classification model to incremental learner

线性二类分类模型

fitTrain linear model for incremental learning
updateMetricsUpdate performance metrics in linear model for incremental learning given new data
updateMetricsAndFitUpdate performance metrics in linear model for incremental learning given new data and train model

朴素贝叶斯模型

fitTrain naive Bayes classification model for incremental learning
updateMetricsUpdate performance metrics in naive Bayes classification model for incremental learning given new data
updateMetricsAndFitUpdate performance metrics in naive Bayes classification model for incremental learning given new data and train model

线性二类分类模型

predictPredict responses for new observations from linear model for incremental learning
lossLoss of linear model for incremental learning on batch of data

朴素贝叶斯模型

predictPredict responses for new observations from naive Bayes classification model for incremental learning
lossLoss of naive Bayes classification model for incremental learning on batch of data
logpLog unconditional probability density of naive Bayes classification model for incremental learning

对象

incrementalClassificationLinearBinary classification linear model for incremental learning
incrementalClassificationNaiveBayesNaive Bayes classification model for incremental learning

主题

Incremental Learning Overview

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.

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

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.