正则化
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,可以使用 lasso
或 ridge
进行正则化最小二乘回归。
为了减少在高维数据集上的计算时间,可以使用 fitrlinear
拟合正则化线性回归模型。
函数
对象
RegressionLinear | Linear regression model for high-dimensional data |
RegressionPartitionedLinear | Cross-validated linear regression model for high-dimensional data |
主题
- Lasso Regularization
See how
lasso
identifies and discards unnecessary predictors. - Lasso and Elastic Net with Cross Validation
Predict the mileage (MPG) of a car based on its weight, displacement, horsepower, and acceleration using
lasso
and elastic net. - Wide Data via Lasso and Parallel Computing
Identify important predictors using
lasso
and cross-validation. - Lasso and Elastic Net
The
lasso
algorithm is a regularization technique and shrinkage estimator. The related elastic net algorithm is more suitable when predictors are highly correlated. - Ridge Regression
Ridge regression addresses the problem of multicollinearity (correlated model terms) in linear regression problems.