정규화(Regularization)
선형 모델에 대한 능형 회귀(Ridge Regression), Lasso, 신축망(Elastic Net)
저차원에서 중간 차원까지의 데이터 세트에 대한 정확도를 높이려면 lasso
또는 ridge
를 사용한 정규화를 통해 최소제곱 회귀를 구현하십시오.
고차원 데이터 세트에 대한 계산 시간을 단축하려면 fitrlinear
를 사용하여 정규화된 선형 회귀 모델을 피팅하십시오.
함수
객체
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.