정규화(Regularization)

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

클래스

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

도움말 항목

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.

Statistics and Machine Learning Toolbox 문서

지원

Mastering Machine Learning: A Step-by-Step Guide with MATLAB

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