正则化

广义线性模型的岭回归、LASSO 和弹性网

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 高效地训练由逻辑回归模型组成的多类纠错输出编码 (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。

函数

lassoglm Lasso or elastic net regularization for generalized linear models

`fitclinear`	Fit binary linear classifier to high-dimensional data
`templateLinear`	Linear learner template
`fitcecoc`	Fit multiclass models for support vector machines or other classifiers
`predict`	Predict labels for linear classification models
`fitckernel`	Fit binary Gaussian kernel classifier using random feature expansion
`predict`	Predict labels for Gaussian kernel classification model

`ClassificationLinear`	Linear model for binary classification of high-dimensional data
`ClassificationECOC`	Multiclass model for support vector machines (SVMs) and other classifiers
`ClassificationKernel`	Gaussian kernel classification model using random feature expansion
`ClassificationPartitionedLinear`	Cross-validated linear model for binary classification of high-dimensional data
`ClassificationPartitionedLinearECOC`	Cross-validated linear error-correcting output codes model for multiclass classification of high-dimensional data

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.
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.