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머신 러닝에서 계층 적 / 중첩 된 데이터를 처리하는 방법
예를 들어 내 문제를 설명하겠습니다. {나이, 성별, 국가, 지역, 도시}와 같은 속성이 주어진 개인의 소득을 예측한다고 가정합니다. 당신은 이와 같은 훈련 데이터 세트를 가지고 있습니다 train <- data.frame(CountryID=c(1,1,1,1, 2,2,2,2, 3,3,3,3), RegionID=c(1,1,1,2, 3,3,4,4, 5,5,5,5), CityID=c(1,1,2,3, 4,5,6,6, 7,7,7,8), Age=c(23,48,62,63, 25,41,45,19, 37,41,31,50), Gender=factor(c("M","F","M","F", "M","F","M","F", "F","F","F","M")), Income=c(31,42,71,65, 50,51,101,38, 47,50,55,23)) train CountryID RegionID CityID Age …
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regression
machine-learning
multilevel-analysis
correlation
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spatial
paired-comparisons
cross-correlation
clustering
aic
bic
dependent-variable
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standard-error
measurement-error
errors-in-variables
regression
multiple-regression
pca
linear-model
dimensionality-reduction
machine-learning
neural-networks
deep-learning
conv-neural-network
computer-vision
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spss
r
weighted-data
wilcoxon-signed-rank
bayesian
hierarchical-bayesian
bugs
stan
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t-test
logit
probit
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confidence-interval
poisson-distribution
deep-learning
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residual-networks
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survey
wilcoxon-mann-whitney
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bias
loss-functions
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