다중 선형 회귀
다중 예측 변수를 사용하는 선형 회귀
저차원에서 중간 차원까지의 데이터 세트에 대한 정확도를 높이려면 fitlm
을 사용하여 선형 회귀 모델을 피팅하십시오.
고차원 데이터 세트에 대한 계산 시간을 단축하려면 fitrlinear
를 사용하여 선형 회귀 모델을 피팅하십시오.
앱
회귀 학습기 | 머신러닝 지도 학습을 사용하여 데이터를 예측하도록 회귀 모델 훈련시키기 |
객체
LinearModel | 선형 회귀 모델 |
CompactLinearModel | Compact linear regression model |
RegressionLinear | Linear regression model for high-dimensional data |
RegressionPartitionedLinear | Cross-validated linear regression model for high-dimensional data |
함수
도움말 항목
선형 회귀 소개
- What Is a Linear Regression Model?
Regression models describe the relationship between a dependent variable and one or more independent variables. - 선형 회귀
선형 회귀 모델을 피팅하고 결과를 검토합니다. - Stepwise Regression
In stepwise regression, predictors are automatically added to or trimmed from a model. - Reduce Outlier Effects Using Robust Regression
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
Choose a regression function depending on the type of regression problem, and update legacy code using new fitting functions. - Summary of Output and Diagnostic Statistics
Evaluate a fitted model by using model properties and object functions. - Wilkinson Notation
Wilkinson notation provides a way to describe regression and repeated measures models without specifying coefficient values.
선형 회귀 워크플로
- Linear Regression Workflow
Import and prepare data, fit a linear regression model, test and improve its quality, and share the model. - 선형 회귀 결과 해석하기
선형 회귀 결과 출력되는 통계량을 표시하고 해석합니다. - Linear Regression with Interaction Effects
Construct and analyze a linear regression model with interaction effects and interpret the results. - Linear Regression Using Tables
This example shows how to perform linear and stepwise regression analyses using tables. - Linear Regression with Categorical Covariates
Perform a regression with categorical covariates using categorical arrays andfitlm
. - Analyze Time Series Data
This example shows how to visualize and analyze time series data using atimeseries
object and theregress
function. - Train Linear Regression Model
Train a linear regression model usingfitlm
to analyze in-memory data and out-of-memory data.
부분 최소제곱 회귀
- Partial Least Squares
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. - 부분 최소제곱 회귀 및 주성분 회귀
부분 최소제곱 회귀(PLSR) 및 주성분 회귀(PCR)를 적용하고 이 두 방법의 효과를 살펴봅니다.