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.
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 |
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.