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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.

회귀 학습기Train regression models to predict data using supervised machine learning


LinearModel선형 회귀 모델
CompactLinearModelCompact linear regression model
RegressionLinearLinear regression model for high-dimensional data
RegressionPartitionedLinearCross-validated linear regression model for high-dimensional data


모두 확장

LinearModel 객체 만들기

fitlm선형 회귀 모델 피팅하기
stepwiselmPerform stepwise regression

CompactLinearModel 객체 만들기

compactCompact linear regression model

선형 모델에서 항 추가 또는 제거

addTermsAdd terms to linear regression model
removeTermsRemove terms from linear regression model
stepImprove linear regression model by adding or removing terms

응답 변수 예측하기

fevalPredict responses of linear regression model using one input for each predictor
predictPredict responses of linear regression model
randomSimulate responses with random noise for linear regression model

선형 모델 평가

anovaAnalysis of variance for linear regression model
coefCIConfidence intervals of coefficient estimates of linear regression model
coefTestLinear hypothesis test on linear regression model coefficients
dwtestDurbin-Watson test with linear regression model object
partialDependenceCompute partial dependence

선형 모델과 요약 통계량 시각화

plotScatter plot or added variable plot of linear regression model
plotAddedAdded variable plot of linear regression model
plotAdjustedResponseAdjusted response plot of linear regression model
plotDiagnosticsPlot observation diagnostics of linear regression model
plotEffectsPlot main effects of predictors in linear regression model
plotInteractionPlot interaction effects of two predictors in linear regression model
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
plotResidualsPlot residuals of linear regression model
plotSlicePlot of slices through fitted linear regression surface

선형 모델의 속성 수집하기

gatherGather properties of machine learning model from GPU

객체 만들기

fitrlinearFit linear regression model to high-dimensional data

RegressionLinear 객체 사용하기

predictPredict response of linear regression model
lossRegression loss for linear regression models
partialDependenceCompute partial dependence
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
selectModelsSelect fitted regularized linear regression models

RegressionPartitionedLinear 객체 사용하기

kfoldLossRegression loss for observations not used in training
kfoldPredictPredict responses for observations not used for training

선형 회귀 피팅 및 평가

dwtestDurbin-Watson test with residual inputs
invpredInverse prediction
linhyptestLinear hypothesis test
plsregressPartial least-squares (PLS) regression
regress다중 선형 회귀
regstatsRegression diagnostics
relieffRank importance of predictors using ReliefF or RReliefF algorithm
robustfitFit robust linear regression
stepwisefitFit linear regression model using stepwise regression

데이터 준비하기

x2fxConvert predictor matrix to design matrix
dummyvarCreate dummy variables

대화형 방식 툴

robustdemoInteractive robust regression
rsmdemoInteractive response surface demonstration
rstoolInteractive response surface modeling
stepwiseInteractive 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

이 예제에서는 부분 최소제곱 회귀(PLSR) 및 주성분 회귀(PCR)를 적용하는 방법을 보여주고 이 두 방법의 효과를 설명합니다.