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正则化

岭回归、LASSO、弹性网

为了提高在中低维数据集上的准确度并增加联系函数选择,可以使用 lassoglm 拟合具有 LASSO 罚分的广义线性模型。

为了减少在高维数据集上的计算时间,可以使用 fitclinear 训练二类线性分类模型,例如正则化逻辑回归模型。还可以使用 fitcecoc 有效地训练由逻辑回归模型组成的多类纠错输出码 (ECOC) 模型。

对于大数据的非线性分类,可以使用 fitckernel 训练带正则化逻辑回归的二类高斯核分类模型。

ClassificationLinearLinear model for binary classification of high-dimensional data
ClassificationECOCMulticlass model for support vector machines (SVMs) and other classifiers
ClassificationKernelGaussian kernel classification model using random feature expansion
ClassificationPartitionedLinearCross-validated linear model for binary classification of high-dimensional data
ClassificationPartitionedLinearECOCCross-validated linear error-correcting output codes model for multiclass classification of high-dimensional data

函数

lassoglmLasso or elastic net regularization for generalized linear models
fitclinearFit linear classification model to high-dimensional data
templateLinearLinear classification learner template
fitcecocFit multiclass models for support vector machines or other classifiers
predictPredict labels for linear classification models
fitckernelFit Gaussian kernel classification model using random feature expansion
predictPredict labels for Gaussian kernel classification model

示例和操作指南

Regularize Poisson Regression

Identify and remove redundant predictors from a generalized linear model.

Regularize Logistic Regression

Regularize binomial regression.

Regularize Wide Data in Parallel

Regularize a model with many more predictors than observations.

概念

Lasso Regularization of Generalized Linear Models

The lasso algorithm produces a smaller model with fewer predictors. The related elastic net algorithm can be more accurate when predictors are highly correlated.