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일반화 선형 회귀

로지스틱 회귀를 비롯해 다양한 분포 및 연결 함수를 포함한 일반화 선형 회귀 모델

저차원에서 중간 차원까지의 데이터 세트에 대한 정확도를 높이고 연결 함수 선택의 폭을 넓히려면 fitglm을 사용하여 일반화된 선형 회귀 모델을 피팅하십시오. 다항 로지스틱 회귀의 경우 fitmnr을 사용하여 모델을 피팅합니다.

고차원 데이터 세트에 대한 계산 시간을 단축하려면 fitclinear를 사용하여 로지스틱 회귀 모델과 같은 이진 선형 분류 모델을 훈련시키십시오. 또한, fitcecoc를 사용하여 로지스틱 회귀 모델로 구성된 다중클래스 오류 수정 출력 코드(ECOC) 모델을 효율적으로 훈련시킬 수도 있습니다.

빅데이터에 대해 비선형 분류를 수행하는 경우 fitckernel을 사용하여 로지스틱 회귀를 적용한 이진 가우스 커널 분류 모델을 훈련시키십시오.

블록

ClassificationLinear PredictClassify observations using linear classification model

함수

모두 확장

GeneralizedLinearModel 객체 만들기

fitglm일반화 선형 회귀 모델 생성
stepwiseglmCreate generalized linear regression model by stepwise regression

CompactGeneralizedLinearModel 객체 만들기

compactCompact generalized linear regression model

일반화 선형 모델에서 항 추가 또는 제거하기

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

응답 변수 예측하기

fevalPredict responses of generalized linear regression model using one input for each predictor
predictPredict responses of generalized linear regression model
random일반화 선형 회귀 모델에 대해 랜덤 잡음이 있는 응답 변수 시뮬레이션하기

일반화 선형 모델 평가하기

coefCIConfidence intervals of coefficient estimates of generalized linear regression model
coefTestLinear hypothesis test on generalized linear regression model coefficients
devianceTestAnalysis of deviance for generalized linear regression model
partialDependenceCompute partial dependence

일반화 선형 모델과 요약 통계량 시각화하기

plotDiagnosticsPlot observation diagnostics of generalized linear regression model
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
plotResidualsPlot residuals of generalized linear regression model
plotSlicePlot of slices through fitted generalized linear regression surface

일반화 선형 모델의 속성 수집하기

gatherGather properties of Statistics and Machine Learning Toolbox object from GPU

MultinomialRegression 객체 만들기

fitmnrFit multinomial regression model

MultinomialRegression 객체 작업

coefCIConfidence intervals for coefficient estimates of multinomial regression model
coefTestLinear hypothesis test on multinomial regression model coefficients
fevalPredict responses of multinomial regression model using one input for each predictor
partialDependenceCompute partial dependence
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
plotResidualsPlot residuals of multinomial regression model
plotSlicePlot of slices through fitted multinomial regression surface
predictPredict responses of multinomial regression model
randomGenerate random responses from fitted multinomial regression model
testDevianceDeviance test for multinomial regression model

객체 만들기

fitclinearFit binary linear classifier to high-dimensional data
fitcecoc서포트 벡터 머신 또는 다른 분류기에 대해 다중클래스 모델 피팅하기
fitckernelFit binary Gaussian kernel classifier using random feature expansion
templateLinearLinear classification learner template

레이블 예측하기

predictPredict labels for linear classification models
predictClassify observations using multiclass error-correcting output codes (ECOC) model
predictPredict labels for Gaussian kernel classification model
glmfit일반화 선형 회귀 모델 피팅
glmvalGeneralized linear model values
mnrfit다항 로지스틱 회귀
mnrvalMultinomial logistic regression values

객체

모두 확장

GeneralizedLinearModelGeneralized linear regression model class
CompactGeneralizedLinearModelCompact generalized linear regression model class
ClassificationLinearLinear model for binary classification of high-dimensional data
ClassificationECOCMulticlass model for support vector machines (SVMs) and other classifiers
ClassificationKernelGaussian kernel classification model using random feature expansion
ClassificationPartitionedLinearCross-validated linear model for binary classification of high-dimensional data
ClassificationPartitionedLinearECOCCross-validated linear error-correcting output codes model for multiclass classification of high-dimensional data

도움말 항목

일반화 선형 회귀

다항 로지스틱 회귀

  • Multinomial Models for Nominal Responses
    A nominal response variable has a restricted set of possible values with no natural order between them. A nominal response model explains and predicts the probability that an observation is in each category of a categorical response variable.
  • Multinomial Models for Ordinal Responses
    An ordinal response variable has a restricted set of possible values that fall into a natural order. An ordinal response model describes the relationship between the cumulative probabilities of the categories and predictor variables.
  • Hierarchical Multinomial Models
    A hierarchical multinomial response variable (also known as a sequential or nested multinomial response) has a restricted set of possible values that fall into hierarchical categories. The hierarchical multinomial regression models are extensions of binary regression models based on conditional binary observations.