다중 선형 회귀

다중 예측 변수를 사용하는 선형 회귀

저차원에서 중간 차원까지의 데이터 세트에 대한 정확도를 높이려면 fitlm을 사용하여 선형 회귀 모델을 피팅하십시오.

고차원 데이터 세트에 대한 계산 시간을 단축하려면 fitrlinear를 사용하여 선형 회귀 모델을 피팅하십시오.

앱

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

모두 확장

`LinearModel` 및 `CompactLinearModel` 함수

`LinearModel` 객체 만들기

`fitlm`	선형 회귀 모델 피팅하기
`stepwiselm`	Perform stepwise regression

`CompactLinearModel` 객체 만들기

compact Compact linear regression model

선형 모델에서 항 추가 또는 제거

`addTerms`	Add terms to linear regression model
`removeTerms`	Remove terms from linear regression model
`step`	Improve linear regression model by adding or removing terms

응답 변수 예측하기

`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

선형 모델 평가

`anova`	Analysis of variance for linear regression model
`coefCI`	Confidence intervals of coefficient estimates of linear regression model
`coefTest`	Linear hypothesis test on linear regression model coefficients
`dwtest`	Durbin-Watson test with linear regression model object
`partialDependence`	Compute partial dependence

선형 모델과 요약 통계량 시각화

`plot`	Scatter plot or added variable plot of linear regression model
`plotAdded`	Added variable plot of linear regression model
`plotAdjustedResponse`	Adjusted response plot of linear regression model
`plotDiagnostics`	Plot observation diagnostics of linear regression model
`plotEffects`	Plot main effects of predictors in linear regression model
`plotInteraction`	Plot interaction effects of two predictors in linear regression model
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`plotResiduals`	Plot residuals of linear regression model
`plotSlice`	Plot of slices through fitted linear regression surface

선형 모델의 속성 수집하기

gather Gather properties of Statistics and Machine Learning Toolbox object from GPU

`RegressionLinear` 및 `RegressionPartitionedLinear` 함수

객체 만들기

fitrlinear Fit linear regression model to high-dimensional data

`RegressionLinear` 객체 사용하기

`predict`	Predict response of linear regression model
`lime`	Local interpretable model-agnostic explanations (LIME)
`loss`	Regression loss for linear regression models
`partialDependence`	Compute partial dependence
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`shapley`	Shapley values
`selectModels`	Select fitted regularized linear regression models

`RegressionPartitionedLinear` 객체 사용하기

`kfoldLoss`	Regression loss for observations not used in training
`kfoldPredict`	Predict responses for observations not used for training

객체를 사용하지 않는 선형 회귀

선형 회귀 피팅 및 평가

`dwtest`	Durbin-Watson test with residual inputs
`invpred`	Inverse prediction
`linhyptest`	Linear hypothesis test
`plsregress`	Partial least-squares (PLS) regression
`regress`	다중 선형 회귀
`regstats`	Regression diagnostics
`relieff`	Rank importance of predictors using ReliefF or RReliefF algorithm
`robustfit`	Fit robust linear regression
`stepwisefit`	Fit linear regression model using stepwise regression

데이터 준비하기

`x2fx`	Convert predictor matrix to design matrix
`dummyvar`	Create dummy variables

대화형 방식 툴

`robustdemo`	Interactive robust regression
`rsmdemo`	대화형 반응 표면 데모
`rstool`	Interactive response surface modeling
`stepwise`	Interactive stepwise regression

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 and fitlm.
Analyze Time Series Data
This example shows how to visualize and analyze time series data using a timeseries object and the regress function.
Train Linear Regression Model
Train a linear regression model using fitlm 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.