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
对象
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 |
主题
- 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.