정규화(Regularization)

선형 모델에 대한 능형 회귀(Ridge Regression), Lasso, 신축망(Elastic Net)

저차원에서 중간 차원까지의 데이터 세트에 대한 정확도를 높이려면 lasso 또는 ridge를 사용한 정규화를 통해 최소제곱 회귀를 구현하십시오.

고차원 데이터 세트에 대한 계산 시간을 단축하려면 fitrlinear를 사용하여 정규화된 선형 회귀 모델을 피팅하십시오.

함수

`lasso`	선형 모델에 대한 Lasso 또는 신축망(elastic net) 정규화
`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

`RegressionLinear`	Linear regression model for high-dimensional data
`RegressionPartitionedLinear`	Cross-validated linear regression model for high-dimensional data

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.