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다중 선형 회귀
저차원에서 중간 차원까지의 데이터 세트에 대한 정확도를 높이려면 fitlm을 사용하여 선형 회귀 모델을 피팅하십시오.
MATLAB® 작업 공간에 담을 수 있는 고차원 데이터 세트에 대한 계산 시간을 단축시키려면 fitrlinear를 사용하여 선형 회귀 모델을 피팅하십시오.
앱
| 회귀 학습기 | Train regression models to predict data using supervised machine learning |
클래스
LinearModel | 선형 회귀 모델 클래스 |
CompactLinearModel | Compact linear regression model |
RegressionLinear | Linear regression model for high-dimensional data |
RegressionPartitionedLinear | Cross-validated linear regression model for high-dimensional data |
함수
fitlm | 선형 회귀 모델 생성 |
stepwiselm | Fit linear regression model using stepwise regression |
compact | Compact linear regression model |
feval | Predict responses of linear regression model using one input for each predictor |
predict | Predict responses of linear regression model |
random | Simulate responses with random noise for linear regression model |
plot | Scatter plot or added variable plot of linear regression model |
plotAdjustedResponse | Adjusted response plot of linear regression model |
fitrlinear | Fit linear regression model to high-dimensional data |
predict | Predict response of linear regression model |
dummyvar | Create dummy variables |
invpred | Inverse prediction |
plotPartialDependence | Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots |
plsregress | Partial least-squares regression |
relieff | Rank importance of predictors using ReliefF or RReliefF algorithm |
x2fx | Convert predictor matrix to design matrix |
regress | 다중 선형 회귀 |
robustdemo | Interactive robust regression |
robustfit | Robust regression |
rsmdemo | Interactive response surface demonstration |
rstool | Interactive response surface modeling |
예제 및 방법
선형 회귀 모델을 피팅하고 결과를 검토합니다.
선형 회귀 결과 출력되는 통계량을 표시하고 해석합니다.
Import and prepare data, fit a linear regression model, test and improve its quality, and share the model.
Regression with Categorical Covariates
Perform a regression with categorical covariates using categorical arrays and
fitlm.
This example shows how to perform linear and stepwise regression analyses using tables.
Robust Regression — Reduce Outlier Effects
Fit a robust model that is less sensitive than ordinary least squares to large changes in small parts of the data.
Choose a regression function depending on the type of regression problem, and update legacy code using new fitting functions.
Partial least squares (PLS) constructs new predictor variables as linear combinations of the original predictor variables, while considering the observed response values, leading to a parsimonious model with reliable predictive power.
이 예제에서는 부분 최소제곱 회귀(PLSR) 및 주성분 회귀(PCR)를 적용하는 방법을 보여주고 이 두 방법의 효과를 설명합니다.
개념
What Is a Linear Regression Model?
Regression models describe the relationship between a dependent variable and one or more independent variables.
Wilkinson notation provides a way to describe regression and repeated measures models without specifying coefficient values.
