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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。
lasso | Lasso or elastic net regularization for linear models |
ridge | Ridge regression |
lassoPlot | Trace plot of lasso fit |
fitrlinear | Fit linear regression model to high-dimensional data |
predict | Predict response of linear regression model |
plotPartialDependence | Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots |
RegressionLinear | Linear regression model for high-dimensional data |
RegressionPartitionedLinear | Cross-validated linear regression model for high-dimensional data |
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.
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 addresses the problem of multicollinearity (correlated model terms) in linear regression problems.